LiDAR SLAM Occupancy Grid Maps
2D and 3D occupancy grid maps generated from handheld and mobile robot 360° LiDAR SLAM scans with loop closure optimization.
Quality & Precision Benchmarks
MAP RESOLUTION
1cm Sub-Centimeter Grid
LOOP CLOSURE YIELD
100% Verified Closed
FORMAT SUPPORT
ROS2, OctoMap, PCD, E57
VALIDATION PASS
100% Passed SymPy Bounds
Dataset Taxonomy & Output Structure
grid_resolution_cm (5cm to 1cm Grid Resolution)
occupancy_probability_array (Float Array [0.0 to 1.0])
slam_graph_node_count (Loop Closure Keyframes)
lidar_channel_count (Hesai 32 / Ouster 64 Channel)
format_export (ROS2 OccupancyGrid / OctoMap)
Specific Type Tasks & Applications
- • Autonomous Mobile Robot (AMR) Navigation
- • BIM Industrial Factory Layout Mapping
- • Warehouse Obstacle & Pathway Vectorization
What Is Right vs What Is Wrong
| COMMON COMPETITOR ERRORS (WRONG) | BLUE PROJECTS GROUND TRUTH (RIGHT) |
|---|---|
| FAIL: Open-loop SLAM trajectories accumulating drift over long corridors creating ghost walls | PASS: Graph-SLAM loop closure optimization maintaining structural alignment across 100,000+ sq ft facilities |
| FAIL: Coarse 50cm occupancy grids unsuitable for narrow warehouse AMR navigation paths | PASS: High-resolution 1cm occupancy grids resolving fine structural pillars and safety barriers |
Files & SLAM Data Example (Python)
import open3d as o3d
# Load Blue Projects SLAM Dataset Type: LiDAR SLAM Occupancy Grid Maps
pcd = o3d.io.read_point_cloud("lidar-slam-occupancy-grid-maps_scan.pcd")
print("Point Cloud Count:", len(pcd.points))
Why Blue Projects for LiDAR SLAM Occupancy Grid Maps?
Request a free matched 10,000 sq ft 360 SLAM point cloud mesh and 8K equirectangular video sample batch.
Request Free Sample Batch →