Multi‑camera SLAM• Multi‑layer optimization• LiDAR fusion• SLAM‑grade localization
Vision–LiDAR SLAM with Multi‑Layer Optimization
We synchronize multi‑camera rigs with scanning LiDAR and IMUs to capture the environment, then solve localization and mapping through a SLAM‑centric pipeline. Local visual–inertial odometry is reinforced by LiDAR odometry; a multi‑layer optimizer (local → submap → global pose‑graph) fuses loop closures, GPS/INS anchors, and LiDAR ICP constraints to deliver centimeter‑grade, repeatable trajectories for analysis and decision‑making.
On top, our multi‑camera analytics extract assets, markings, and structures, quantify change, and export GIS‑ready layers.
How it works
- Sense: synchronized multi‑camera + LiDAR + IMU capture time‑aligned RGB, depth, and point clouds.
- Calibrate & time‑sync: intrinsics/extrinsics for all sensors; clock alignment for precise fusion.
- Front‑end odometry: visual–inertial odometry (VIO) + LiDAR odometry provide robust motion in all conditions.
- Build the graph: keyframes, loop‑closures, LiDAR ICP matches, and GPS/INS priors compose a factor graph.
- Multi‑layer optimization: local windows → submap alignment → global pose‑graph for drift‑resistant localization.
- Reconstruct & register: fuse frames and scans into dense, colorized 3D meshes and point clouds.
- Analyze: multi‑camera perception extracts assets, markings, clearances, and changes across runs.
- Deliver: GIS‑ready layers and CAD/BIM exports (GeoPackage, LAS/LAZ, WGS84/OSGB) with reproducible accuracy.
Why SLAM + LiDAR + Multi‑camera
Accurate localization
Multi‑layer optimization stabilizes trajectories with loop closures and GPS anchors.
Geometry in all light
LiDAR complements cameras at night, glare, or texture‑poor scenes.
Consistent structure
Submaps and global alignment reduce drift across long routes.
Rich environmental data
Multi‑camera views improve detection, classification, and change analysis.
Survey‑friendly outputs
Align to control; export to GIS and CAD/BIM with audit trails.
Operational scale
Edge + cloud pipelines process city‑scale datasets and repeat surveys.
What you get
- Globally consistent 3D point clouds (LAS/LAZ), colorized meshes, and intensity maps.
- GIS layers for assets (lighting columns, signs, furniture), lane/marking vectors, and work zones.
- Change‑detection reports and dashboards across repeat runs.
- Perception‑ready datasets (time‑synced images, LiDAR, poses) for localization and ML training.
- Web viewers and APIs for integration with asset management and digital twins.
Consistent geometry
Stable structure over distance
Multi‑layer SLAM
Local, submap, global
Privacy‑first
Face/plate blurring on‑device
Cloud or on‑prem
Batch at scale or run edge‑only
Tech highlights
Factor‑graph backend
Joint optimization over VIO, LiDAR ICP, GPS/INS, and loop closures.
Multi‑camera front‑end
Synchronized views improve robustness, coverage, and semantics.
Time‑sync & calibration
Camera–LiDAR–IMU extrinsics and clock sync for precise fusion.
Loopback refinement
Segment‑wise clean‑velocity anchors reduce drift across sessions.
Dataset tooling
Export pose‑tagged frames and fused trajectories for downstream algos.
Standards‑based export
GeoPackage, LAS/LAZ, WGS84/OSGB, with reproducible metadata.
Bring SLAM‑grade Vision–LiDAR Fusion to your fleet
Deploy on vehicles, carts, or backpacks. Integrate with asset systems and survey control to reach your accuracy targets.
Multi‑camera
LiDAR
GNSS/INS
Multi‑layer
Edge & Cloud
Smart City