Part 4: Mastering Technique

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Quick Field Guide

Initialization - Critical Foundation

Why Initialization Matters:

• Establishes IMU calibration for entire scan
• Creates initial map reference frame
• Sets coordinate system origin
• Poor initialization = uncorrectable errors throughout scan

Position Selection Criteria:

✅ Good Initialization Positions:

• Flat, stable surface (concrete, asphalt, compacted soil)
• 360° clear view with features at 2-10m distance
• Structured features visible (walls, equipment, architecture)
• RTK clear sky view (if using RTK, >15° elevation)

❌ Avoid:

• Carpet, loose gravel, flexible floors
• Empty open areas with features >20m away
• Tight corners blocking >180° of view
• Blank walls providing features in one direction only

Learn more about initialization →

Initialization Procedure:

1. Place steel base on flat surface
2. Clear 3m radius around scanner (no people/objects)
3. Place scanner vertically on base
4. Start scan in LixelGO
5. Remain motionless during 15-second countdown
6. Wait additional 15 seconds after "initialization complete"
7. Lift scanner slowly and smoothly (2-3 seconds)
8. Stand upright, scanner at chest height
9. Begin walking SLOWLY (0.5 m/s for first 10-15 seconds)
10. Increase to normal speed after tracking established

Common Initialization Errors:

• Moving during 15-second countdown → IMU bias errors (unfixable)
• Insufficient post-initialization dwell → immediate tracking loss
• Poor feature visibility → unstable tracking throughout scan
• Jerky lifting motion → IMU disturbance

Learn more about initialization execution →

Device Orientation Requirements

Vertical Orientation is Mandatory:

• LiDAR at top, battery at bottom
• Perpendicular to ground
• Target: <15° data-preserve-html-node="true" tilt (green indicator in LixelGO)
• Warning: 15-20° tilt (yellow indicator)
• Excessive: >20° tilt (red indicator - correct immediately)

Why Vertical Matters:

• RTK antenna must point up for satellite reception
• IMU calibrated for vertical operation
• LiDAR field of view optimized for vertical
• Tilt >20° degrades RTK, confuses IMU, misses coverage

Exceptions (Brief Tilting Allowed):

• Ground-level targets: tilt forward 20-30° for 10-20 seconds
• Overhead targets: tilt backward 20-30° briefly
• Always return to vertical immediately after

Learn more about device orientation →

Walking Speed Guidelines

Standard Speed: 1 m/s (3.6 km/h)

• Relaxed casual walking pace
• Balances coverage vs. quality
• Maintain consistency - avoid sudden changes

Reduce Speed to 0.5-0.7 m/s When:

• Feature-poor environments (empty rooms, plain corridors)
• Narrow corridors (<2m data-preserve-html-node="true" width)
• Scanning detailed features
• Transitions (doorways, stairs, level changes = 0.3-0.5 m/s)

Increase Speed Carefully:

• Feature-rich environments can support 0.8-1.2 m/s
• Never sacrifice quality for speed
• Outdoor sites with clear features support normal speeds

Learn more about walking speed →

Rotation Control

Smooth Rotation Guidelines:

• Keep rotation <180° data-preserve-html-node="true"/second (90° turn in 0.5 sec)
• Brief maneuvers can reach 360°/second max
• Pivot whole body, not just arms
• Wide gentle turns at corners (start 1-2m before corner)
• 180° reversals over 1-2 seconds (3-4 steps while rotating)

Why Smooth Rotation Matters:

• Rapid spinning can exceed IMU measurement range
• SLAM algorithm needs time to track features
• Jerky rotation creates integration errors

Distance From Objects

General Rule: 0.5-3 meters from walls/objects

• Optimal: 1-2 meters (2000-4000 pts/m²)
• Minimum: 0.5m (prevents scanner body occlusion)
• Maximum: 3m normal (adequate density for most work)

Exceptions:

• Detailed features: Approach to 0.3-0.5m briefly
• Large open areas: Maintain closest practical distance
• Point density at 3m = 1000-2000 pts/m²
• Point density at 10m = 100-200 pts/m²

Body Position & Grip

Standard Posture:

• Scanner in front of body at chest-to-shoulder height
• Two-handed grip (battery + body/phone mount)
• Device vertical
• Maximum forward LiDAR visibility

Narrow Corridor Exception:

• When width <2m, data-preserve-html-node="true" hold scanner to side (left or right)
• Allows LiDAR to see both near and far walls
• Switch back to front position in wider areas

Field of View Management:

• Scanner body blocks ~30-40° horizontal
• Operator body blocks ~60-90° behind
• Never block >90° of horizontal FOV → tracking loss risk

Route Planning Principles

Structure-First, Details-Later:

• Buildings: Corridors first → rooms second
• Outdoor: Perimeter first → interior sections second
• Establishes framework before adding complexity

Loop Closure is Critical:

• Linear paths = error accumulation (no correction)
• Loop paths = error correction at closure points
• Return to previously scanned areas regularly
• Multiple smaller loops > single large loop
• Multi-loop routes achieve 30-50% better accuracy

Learn more about route planning →

Building Interiors - Corridor-First Approach:

1. Scan all accessible corridors (creates floor layout loop)
2. Return to start after corridor loop
3. Enter individual rooms one at a time
4. Scan room and return to corridor before next room

Multi-Floor Buildings - Stairwell-First:

1. Scan ground floor corridors
2. Scan complete stairwell ascending to next floor
3. Scan next floor corridors
4. Continue to next stairwell and ascend
5. Creates strong vertical connection between floors

Outdoor Sites - Perimeter-First:

1. Walk complete site perimeter (1-2m inside boundary)
2. Return to start (closes perimeter loop)
3. Plan interior crossing paths to divide site
4. Scan each section methodically

Doorway & Transition Technique

Doorway Crossing Procedure:

1. Approach doorway from current room
2. Stop IN doorway
3. Orient sideways (perpendicular to door direction)
4. Stand motionless 10-15 seconds
5. Proceed through to next room

Why Sideways?

• LiDAR sees both previous AND next room simultaneously
• Maintains SLAM tracking through transition
• Facing forward/backward only sees one room

Stairwell Technique:

• Stand at base 10-15 seconds
• Ascend/descend at 0.5 m/s (half normal speed)
• Keep scanner vertical (tilt body to compensate for incline)
• Pause briefly at each landing (5-10 seconds)
• Exit slowly, stand sideways in doorway 10-15 seconds

Environmental Challenges

Feature-Poor Environments:

• Long corridors, empty warehouses, parking garages, open fields

Mitigation Strategies:

1. Add temporary features (cones, markers, boxes)
2. Reduce speed to 0.3-0.5 m/s
3. Increase control point density (30-50m spacing vs. 100m)
4. Enable Narrow Scene mode in LixelStudio (Settings)
5. Multiple overlapping passes from different positions
6. Supplement with manual measurements for critical dimensions

Learn more about feature-poor environments →

Dynamic Objects (People, Vehicles):

• Cause ghosting in point cloud
• Can confuse SLAM tracking

Mitigation:

1. Schedule scanning during low-activity periods
2. Control access temporarily (coordinate with facility)
3. Orient scanner away from active areas
4. Pause scan during heavy activity (double-click power)
5. Enable Dynamic Object Removal in LixelStudio processing

Learn more about dynamic objects →

Glass & Reflective Surfaces:

• LiDAR has fundamental limitations with these materials
• Creates gaps, doubled geometry, specular reflections

Mitigation:

1. Scan from oblique angles (30-60° vs. perpendicular)
2. Accept incomplete data (document limitation)
3. Supplement with photography for glass locations
4. Manual measurement for critical glass dimensions

Learn more about glass surfaces →

Lighting Variations (Camera Coloring):

• LiDAR works in darkness, but cameras need light
• Overexposure in bright sun, underexposure in shadow

Mitigation:

1. Consistent lighting throughout session
2. Avoid bright-to-dark transitions (walk slowly through)
3. Minimize backlighting (plan route with light behind/side)
4. Overcast days ideal for outdoor color scanning
5. Disable camera coloring if lighting too poor
6. Consider external camera coloring with controlled lighting

Learn more about lighting →

Key Technique Reminders

• Initialization quality = foundation of entire scan
• Vertical orientation mandatory (<15° data-preserve-html-node="true" tilt target)
• Smooth consistent movement (1 m/s standard)
• Loops > linear paths (enables error correction)
• Doorways: stop, sideways, wait 10-15 sec
• Feature-poor: slow down, add features, more control points
• Accept material limitations (glass, reflective surfaces)
• Adapt technique to environment (not one-size-fits-all)

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Full Explanatory Guide

Understanding Scanning Technique Fundamentals

SLAM scanning requires specific operational techniques that differ fundamentally from static terrestrial laser scanning, total station surveying, and photogrammetry workflows. Excellence in SLAM scanning comes from understanding these differences and adapting technique accordingly. Poor technique cannot be corrected in post-processing and directly determines final data quality.

The key differences between SLAM and static scanning include:

• Continuous motion during data capture rather than stationary positions
• Real-time trajectory estimation rather than known instrument positions
• Feature-based localization rather than known survey points
• Accumulation of small errors over time that require correction through loop closures rather than independent measurements at each position

These fundamental differences mean that operator technique directly influences the SLAM algorithm's ability to maintain accurate tracking. Static scanning places minimal demands on operator technique once the tripod is level and the scanner is positioned. SLAM scanning requires continuous awareness of device orientation, movement speed, feature visibility, and route planning throughout the data capture process.

Scanner Initialization Phase

Every scan session begins with initialization, and this phase establishes the fundamental reference frame that all subsequent measurements depend on. Poor initialization introduces errors that cannot be fully corrected in post-processing regardless of how sophisticated the algorithms are. Excellent initialization establishes the foundation for successful scanning.

Why Initialization Quality Matters

During the 15-second initialization countdown, the scanner executes multiple critical operations simultaneously:

IMU calibration: The accelerometer and gyroscope calibrate their bias estimates to establish what "stationary" means in the current orientation. Any movement during initialization corrupts these calibration processes. If movement occurs, the estimated biases incorporate motion as if it were sensor error, degrading all subsequent IMU measurements.

Initial map construction: The LiDAR accumulates initial point cloud data defining the static environment around the starting position. The SLAM algorithm builds the initial map representation that will be extended as scanning progresses. If people or objects move during initialization, the SLAM algorithm incorporates them as static features, then becomes confused when those "static" features disappear or change position.

Coordinate system establishment: The coordinate system origin is established at the scanner's position with axes aligned to the device orientation.

The initialization quality directly determines the scanner's ability to maintain tracking during the first 30-60 seconds of movement. Weak initialization with inadequate feature density or corrupted IMU calibration increases the risk of tracking loss when movement begins. Strong initialization with rich features and accurate calibration provides robust tracking throughout the scan session.

Initialization Position Selection

Selecting the initialization position requires evaluating multiple factors:

Surface stability represents the primary criterion. The base supporting the scanner must be rigid and level to prevent movement during the 15-second countdown and the 15-second post-initialization dwell.

Ideal surfaces:

• Concrete floors in good condition with no cracks or spalling
• Asphalt pavement that is compacted and stable
• Compacted soil or gravel that shows no movement when walked on

Problematic surfaces to avoid:

• Carpet or other flexible flooring that compresses under weight
• Loose gravel or soil that shifts when stepped on
• Wooden floors or platforms that flex or vibrate
• Metal grating that resonates
• Grass or vegetation that compresses and springs back
• Any surface with visible cracks or instability

When no ideal surface is available, the steel control point base can be placed on the most stable area available. However, extra care is required to ensure the base sits solidly without rocking. On uneven surfaces, identify the highest three points of contact and verify the base makes firm contact at all three points without wobbling.

Feature visibility from the initialization position must provide unobstructed 360-degree view of structured geometric features. The SLAM algorithm requires distinctive features for robust tracking.

Optimal initialization positions have:

• Structured features (walls, equipment, furniture, architectural details) at 2-10 meters distance in all directions
• Unobstructed line of sight for 360 degrees around the scanner with no objects closer than 1 meter
• Variety in feature types and distances rather than uniform blank surfaces

Problematic initialization positions:

• Center of large empty rooms or fields where nearest walls are 20+ meters distant
• Directly in front of blank walls providing features in only one direction
• Tight corners where walls or equipment obstruct more than 180 degrees of view
• Areas with moving people or vehicles within 3 meters during initialization

The feature distance guideline of 2-10 meters stems from LiDAR performance characteristics:

• At distances under 1 meter: Some LiDAR models have reduced accuracy or minimum range limitations
• At distances under 2 meters: A small number of surfaces dominate the field of view
• At distances over 10 meters: Point density decreases and small features become harder to distinguish
• At distances over 20 meters: Insufficient geometric detail for robust initialization

RTK antenna visibility (when using RTK positioning) adds an additional requirement. The RTK antenna must have unobstructed view of the sky for satellite reception. Buildings, trees, overhangs, and other obstacles above the scanner degrade RTK performance. Select positions with clear sky view in all directions above approximately 15 degrees elevation.

Initialization Execution Procedure

Executing initialization correctly requires following specific steps in sequence:

Step 1: Place the steel control point base on the selected initialization position. The base should sit flat without rocking. If rocking occurs, reposition the base or select a different initialization location. The base's sharp front corner should point generally toward the area to be scanned.

Step 2: Clear the initialization area. Walk a complete circle around the scanner at 3 meters radius. Remove any objects that can be moved. Ask people to step back beyond the 3-meter radius. Verify nothing obstructs the 360-degree horizontal view from the scanner.

The 3-meter clearance radius stems from LiDAR field of view and SLAM algorithm requirements. Objects within 1 meter may be outside the LiDAR minimum range or may block significant portions of the field of view. Objects within 3 meters dominate the initial map construction. If these objects move during or immediately after initialization, the SLAM algorithm loses essential reference features.

Step 3: Place the scanner on the control point base. The device should sit vertically with the LiDAR assembly on top and battery handle at the bottom. Verify the scanner is stable and will not tip over.

Step 4: Initiate the scan in LixelGO. Enter the project name if prompted. The 15-second initialization countdown begins immediately after clicking the start button.

Step 5: Remain completely motionless during the 15-second countdown. Do not touch the scanner except for minimal stabilization if absolutely necessary to prevent tipping. If stabilization is required, grip only the battery handle and control point base with minimal pressure. Do not apply rotational forces.

Watch the countdown timer decrement from 15 to 0. When the timer reaches 0, LixelGO displays "Static initialization complete."

Step 6: Wait an additional 15 seconds after the "initialization complete" message appears. Do not immediately begin moving the scanner. This post-initialization dwell period allows the SLAM algorithm to accumulate sufficient point cloud data and establish tracking stability before movement begins.

The point cloud preview in LixelGO populates during this period, showing the surrounding environment as colored dots. Elevation coloring applies a color gradient based on height, with blues representing lower elevations and reds representing higher elevations.

Step 7: After the 15-second post-initialization dwell, begin movement slowly. Lift the scanner from the control point base using both hands. One hand grasps the battery while the other supports the device body or stabilizes the phone mount. Bring the scanner to normal handheld position in front of the body at approximately chest to shoulder height.

Step 8: Initial movement should be slow and deliberate for the first 10-15 seconds. Walk at approximately half your normal walking pace (0.5 m/s) while the SLAM algorithm establishes tracking confidence with the device in motion.

Step 9: After 10-15 seconds of successful tracking, increase to normal walking speed of approximately 1 meter per second.

Movement and Posture During Scanning

Proper movement technique and device posture maintain SLAM tracking quality throughout the scan session.

Device Orientation Requirements

Vertical orientation is mandatory during normal scanning. The scanner should remain perpendicular to the ground with the LiDAR assembly on top and battery handle at the bottom.

Target tilt: <15 data-preserve-html-node="true" degrees from vertical** (green indicator in LixelGO)
**Warning tilt: 15-20 degrees** (yellow indicator)
**Excessive tilt: >20 degrees (red indicator - correct immediately)

Why vertical orientation matters:

RTK antenna positioning: The RTK antenna is located at the top of the device and must point upward to receive satellite signals. Tilting the device reduces the effective antenna gain in the satellite direction and can cause loss of RTK fix.

IMU calibration: The IMU is calibrated assuming vertical operation. The accelerometers measure gravity vector to determine the device's orientation. Tilting the device changes the gravity vector relative to the sensor axes, introducing errors in the orientation estimate.

LiDAR field of view: The LiDAR sensor's field of view is optimized for vertical operation to capture floor, walls, and ceiling simultaneously. Excessive tilt shifts the field of view upward or downward, potentially missing important features.

Exceptions allowing brief tilting:

Ground-level targets requiring detailed capture (floor drains, ground markings, low equipment) can be scanned by tilting the device forward 20-30 degrees for 10-20 seconds. Return to vertical position immediately after capturing the ground-level feature.

Overhead targets (ceiling details, overhead utilities, tall equipment) can be scanned by tilting the device backward 20-30 degrees briefly. Again, return to vertical immediately.

These brief tilting exceptions should be used sparingly and only when necessary to capture specific features that cannot be adequately captured with vertical orientation.

Walking Speed Guidelines

Standard walking speed: 1 meter per second (3.6 km/h) represents a relaxed casual walking pace. This speed balances coverage efficiency with point density and SLAM tracking robustness.

Maintain consistency in walking speed. Sudden changes in speed can confuse the SLAM algorithm's velocity estimation and introduce small trajectory errors. If speed adjustment is needed, accelerate or decelerate gradually over several seconds rather than abrupt changes.

Reduce speed to 0.5-0.7 m/s when:

Feature-poor environments (empty rooms, plain corridors, parking garages) benefit from slower movement to increase point density and provide more time for the SLAM algorithm to identify subtle features.

Narrow corridors under 2 meters width require slower movement to ensure adequate point density on both walls simultaneously.

Detailed feature scanning (equipment faces, architectural details, complex geometry) requires slower movement to increase point density in areas requiring high detail.

Transitions (doorways, stairs, level changes) are critical tracking moments requiring reduced speed. At doorway thresholds, reduce to 0.3-0.5 m/s to ensure adequate feature overlap between rooms.

Increase speed carefully:

Feature-rich environments with abundant geometric detail can support speeds of 0.8-1.2 m/s while maintaining adequate point density and tracking quality. However, never sacrifice data quality for speed. Outdoor sites with clear structural features and good visibility support normal or slightly faster speeds.

Rotation Control

Smooth rotation guidelines:

Keep rotation under 180 degrees per second as a general rule. A 90-degree turn executed over 0.5 seconds represents the normal rotation rate during walking. Brief maneuvers can reach 360 degrees per second maximum, but this should be exceptional rather than routine.

Pivot your whole body rather than just rotating your arms while holding the scanner. Body pivoting produces smoother, more controlled rotation that the IMU can track accurately.

Wide gentle turns at corners: Start turning 1-2 meters before reaching the corner rather than making a sharp 90-degree turn at the corner itself. This gradual turning maintains better feature tracking through the corner transition.

180-degree reversals should be executed over 1-2 seconds, equivalent to 3-4 walking steps while rotating. Rapid spinning on one foot creates excessive rotational velocity that can exceed IMU measurement range.

Why smooth rotation matters:

The IMU gyroscopes have maximum measurement ranges. Rapid spinning can exceed these ranges, causing saturation and temporary loss of rotation rate data. The SLAM algorithm requires time to observe and track features as they move through the field of view. Jerky rotation moves features too quickly for reliable tracking. Integration errors accumulate when rotation rate estimates are poor.

Route Planning for Different Environments

Route planning strategy significantly impacts final point cloud quality and SLAM tracking success. Well-planned routes incorporate structure-first principles and loop closure opportunities.

Structure-First, Details-Later Principle

Buildings: Scan corridors first to establish the floor layout framework. Then enter individual rooms to capture details. This sequence ensures the overall building structure is captured robustly before adding complexity.

Outdoor sites: Walk the perimeter first to establish site boundaries and overall layout. Then scan interior sections. This framework-first approach prevents getting lost in complex interior areas before establishing the broader context.

Loop Closure Principle

Linear paths accumulate error without correction opportunity. As SLAM tracking progresses along a linear path, small errors accumulate in the position estimate. Without returning to previously mapped areas, these errors persist in the final result.

Loop paths enable error correction. When the scanner returns to a previously mapped area, the SLAM algorithm recognizes the re-observed features and detects any accumulated drift. The algorithm can then correct the trajectory to make the loop close properly, distributing the correction across the entire loop.

Return to previously scanned areas regularly throughout the scan session. Multiple smaller loops provide more correction opportunities than a single large loop. Multi-loop routes achieve 30-50% better accuracy compared to equivalent linear routes.

Building Interior Route Planning

Corridor-First Approach:

1. Scan all accessible corridors to create a complete floor layout loop
2. Return to the starting position after completing the corridor loop
3. Enter individual rooms one at a time from the corridor
4. Scan each room thoroughly and return to the corridor before proceeding to the next room

This approach ensures strong tracking throughout because the corridor network provides consistent reference features. Each room connection back to the corridor creates a mini-loop that corrects any drift accumulated while scanning the room.

Multi-Floor Buildings - Stairwell-First:

1. Scan ground floor corridors completely
2. Scan the complete stairwell ascending to the next floor (capturing all landings and transitions)
3. Scan the next floor corridors
4. Continue to the next stairwell and ascend to the next floor
5. Repeat for each additional floor

This approach creates strong vertical connections between floors through the stairwells. The stairwell acts as the vertical analog of corridors on each floor, tying the floors together into a coherent 3D structure.

Outdoor Site Route Planning

Perimeter-First Approach:

1. Walk the complete site perimeter 1-2 meters inside the boundary
2. Return to the starting position to close the perimeter loop
3. Plan interior crossing paths that divide the site into sections
4. Scan each section methodically with local loops
5. Return to previously scanned paths regularly to create closure opportunities

The perimeter establishes the overall site extent and provides boundary references. Interior crossing paths then divide the site into manageable sections that can be scanned systematically.

Feature-Poor Environments

Feature-poor environments present special challenges for SLAM tracking because the algorithm has limited geometric information to work with. Understanding these challenges and implementing appropriate mitigation strategies prevents tracking loss and quality degradation.

Common feature-poor environments include:

• Long corridors with plain walls and no doors or variation
• Empty warehouses before equipment installation
• Parking garages with repetitive identical structure
• Underground tunnels and mines
• Open fields with flat uniform terrain
• Newly poured concrete pads with no markings or variation

Mitigation Strategies

Strategy 1: Add temporary features before scanning. Construction cones placed at regular intervals along corridors, survey markers or painted marks on floors or walls, cardboard boxes or barrels positioned strategically, and temporary equipment or materials already on site that can be positioned in feature-poor areas. These temporary features are captured in the scan and provide geometric references for SLAM tracking. They can be manually removed from the point cloud during post-processing if they should not appear in the deliverable.

Strategy 2: Reduce walking speed to 0.3-0.5 meters per second. Slower walking increases point density and provides more time for the SLAM algorithm to identify subtle features that might be missed at higher speeds. The extended observation time also reduces the impact of individual measurement noise.

Strategy 3: Increase control point density. In feature-poor environments, control points should be placed every 30-50 meters instead of the standard 100 meters for L2 Pro (or 15-30 meters instead of 50 meters for K1). The increased control point density provides more external position constraints that help the SLAM algorithm correct drift.

Strategy 4: Enable Narrow Scene mode in LixelStudio processing settings. This mode is specifically designed for tunnels, mines, and long corridors. Navigate to Project Processing → Advanced Settings → Scene Type → Narrow Scene. This mode adjusts the SLAM algorithm to be more conservative about position estimates, improving robustness in feature-limited environments. The tradeoff is slightly longer processing time.

Strategy 5: Create multiple overlapping passes. Rather than scanning a long corridor in a single pass, walk down the corridor (first pass), return to the start via a parallel path (second pass), and repeat with additional offset paths if needed. Multiple passes from slightly different positions provide multiple perspectives on the limited features available.

Strategy 6: Combine scanning with supplementary measurements. In extremely feature-poor environments where SLAM may struggle despite all mitigation efforts, supplement scanning with conventional measurements using a laser distance meter or tape measure. These measurements provide independent verification of critical dimensions and can detect if SLAM drift has occurred.

Dynamic Objects and Moving People

The SLAM algorithm assumes the environment is static. Dynamic objects violate this assumption and introduce noise into the scan data and potential confusion in SLAM tracking.

Dynamic object effects:

• Noise in the final point cloud where moving objects appear at multiple positions (ghosting)
• Confusion in SLAM tracking if the dynamic object obscures static features
• Potential tracking loss if large dynamic objects dominate the field of view

Common sources:

• People walking through the scan area
• Vehicles moving in parking lots or on roads
• Doors opening and closing
• Industrial equipment operating during scanning
• Construction activities
• Animals in outdoor scans

Mitigation Strategies

Strategy 1: Schedule scanning during low-activity periods. For commercial buildings, scan during off-hours (evenings, weekends, holidays). For industrial facilities, coordinate with operations to identify low-activity shifts or planned downtime. For outdoor sites near roads, early morning typically has minimal traffic.

Strategy 2: Control access temporarily. Coordinate with facility management to restrict access to areas being scanned. Post temporary barriers or signs requesting people avoid the area during scanning. Assign a team member to manage access at key points.

Strategy 3: Orient the scanner away from major sources of movement. When scanning near active areas, position yourself so the scanner faces toward static areas rather than toward active areas. Static areas dominate the LiDAR field of view, while active areas appear only in peripheral view.

Strategy 4: Pause scanning during heavy activity. If a large group of people enters the scan area, or a vehicle blocks the scanning path, pause the scan using the double-click power button sequence. Wait for the activity to clear, then resume with another double-click. Note that frequent pausing and resuming can introduce small discontinuities.

Strategy 5: Enable Dynamic Object Removal in LixelStudio processing. Navigate to Project Processing → Advanced Settings → Dynamic Object Removal → Enable. This filter attempts to identify and remove inconsistent points that likely represent moving objects. However, it cannot prevent SLAM tracking degradation caused by dynamic objects during scanning.

Strategy 6: Accept that some dynamic object noise is unavoidable in active environments. Document the presence of dynamic objects in project notes and deliverable documentation. For many applications, moderate dynamic object noise does not prevent the point cloud from being useful.

Glass and Reflective Surfaces

LiDAR operates by measuring the time for laser pulses to return after reflecting from surfaces. Glass and highly reflective surfaces create unique challenges.

Glass effects:

• Laser pulses passing through glass with no return (creating gaps)
• Laser pulses reflecting from both front and back surfaces (creating doubled geometry)
• Laser pulses reflecting from surfaces behind glass (creating offset geometry)
• Laser pulses reflecting at specular angles (creating unexpected returns)

Highly reflective surfaces like polished metal, mirrors, glossy painted surfaces, and still water exhibit similar effects with specular reflection dominating.

Mitigation Strategies

Strategy 1: Scan from oblique angles rather than perpendicular to glass surfaces. When the LiDAR beam hits glass at a perpendicular angle (90 degrees), reflection and transmission effects are strongest. When the beam hits at an oblique angle (30-60 degrees from perpendicular), more energy tends to reflect back to the sensor. Plan routes that approach glass facades at angles rather than head-on.

Strategy 2: Accept that glass areas will have incomplete or noisy data. This is a fundamental limitation of LiDAR technology for these materials. Document in deliverables that glass and reflective surfaces should be interpreted carefully. Note that glass locations should be verified with as-built drawings or supplementary measurements.

Strategy 3: Supplement LiDAR with photography for glass features. Conventional photographs clearly show glass locations, frames, and extent even though the point cloud may have gaps. Providing annotated photographs alongside point cloud deliverables helps users understand glass locations.

Strategy 4: Use manual measurement or traditional surveying for critical glass dimensions. If precise dimensions of glass panels or reflective surfaces are required, measure them directly with a tape measure or laser distance meter rather than extracting from the point cloud.

Strategy 5: For large glass facades, if budget and schedule permit, consider applying temporary treatments that increase reflectivity. Architectural surveying sometimes uses washable sprays or temporary adhesive films that make glass more LiDAR-visible. However, this approach is labor-intensive and requires permission.

Lighting Variations and Camera Coloring Quality

The LiDAR sensor operates independently of lighting conditions and functions in complete darkness. However, if camera coloring is enabled, lighting variations affect color quality significantly.

Camera challenges in varying light:

• Overexposure in bright sunlight washing out detail
• Underexposure in deep shadow losing detail
• Harsh shadows creating dramatic contrast exceeding camera dynamic range
• Color temperature variations creating inconsistent color across the scan

Mitigation Strategies

Strategy 1: Maintain consistent lighting throughout the scan session. Indoors, turn on all lights before starting the scan and leave them on throughout. Do not scan half of a building during daytime with natural light and the other half in evening with artificial light only. Outdoors, the most consistent lighting occurs on overcast days where diffuse cloud cover eliminates harsh shadows.

Strategy 2: Avoid sudden transitions between bright and dark areas when possible. When scanning must transition from bright outdoor areas to dim indoor areas, walk slowly through the transition zone to allow the camera exposure to adjust gradually. The camera's automatic exposure adjustment takes several seconds to respond to dramatic lighting changes.

Strategy 3: Plan scan routes to minimize backlighting situations. Backlighting occurs when the scanner faces toward bright light sources (sun, windows, light fixtures) while trying to capture darker surfaces in the foreground. Plan routes that scan with light sources behind or to the side when possible.

Strategy 4: Accept that harsh lighting conditions will produce lower color quality. Document lighting challenges in project notes. For projects where color accuracy is critical, specify ideal lighting conditions in the scope and schedule accordingly.

Strategy 5: Disable camera coloring for scans where lighting makes color quality unacceptable. If lighting conditions are very poor, disable camera coloring in LixelGO before scanning. The resulting point cloud will be uncolored or use intensity-based coloring only. If color is required for deliverables, the scan can be repeated later under better lighting conditions.

Strategy 6: Use external camera coloring with controlled lighting. For projects requiring highest color quality, consider using external panoramic photography with professional lighting instead of the built-in scanner cameras. External photos can be captured under ideal lighting and then projected onto the geometry in LixelStudio.