- GENESIS + SYNTHESIS
- REAL-TIME SLAM
- GAUSSIAN SPLATS
PRISM
MULTI-SENSOR GAUSSIAN SLAM
Multi-sensor Gaussian SLAM fusing LiDAR, camera, and IMU for real-time photorealistic scene mapping. Continuous trajectory estimation, incremental Gaussian map construction, RGB/depth rendering with geometry export. One unified pipeline from raw sensor streams to photorealistic 3D reconstruction.
MODULE STATUS: DEVELOPMENTSLAM Mode
3FUSE
- DIVISION
- ANIMA
- WAVE
- W5
- DOMAIN
- HARDWARE & SENSORS
- WAVE 5 // ANIMA SUITE
- PERCEPTION — MULTI-SENSOR GAUSSIAN SLAM
TRADITIONAL SLAM PRODUCES SPARSE, LIFELESS MAPS
Classical SLAM systems output sparse point clouds or occupancy grids — functional for navigation but useless for photorealistic rendering, simulation, or detailed scene understanding. Camera-only approaches lack geometric precision. LiDAR-only methods miss appearance. Neither alone produces the dense, textured reconstructions that modern robotics demands.
Real-world deployment needs maps that are simultaneously geometrically accurate, photorealistically renderable, and exportable as meshes. This requires fusing heterogeneous sensors — LiDAR for geometry, cameras for appearance, IMU for motion — into a single coherent Gaussian representation, all running in real-time.
WHAT PRISM DELIVERS
PRISM is a multi-sensor Gaussian SLAM system that incrementally builds photorealistic 3D maps from LiDAR point clouds, camera images, and IMU data. Continuous trajectory estimation feeds an incremental Gaussian map builder that supports RGB rendering, depth rendering, and mesh export.
CAPABILITIES
- LiDAR + Camera + IMU tight fusion — continuous trajectory estimation with sub-centimeter accuracy
- Incremental Gaussian map construction — new splats initialized from LiDAR depth, refined with camera RGB
- Real-time RGB and depth rendering from the Gaussian map at arbitrary viewpoints
- Mesh export pipeline — Gaussian-to-mesh conversion for CAD, simulation, and downstream planning
- Adaptive splat density — automatic densification in detail-rich regions, pruning in empty space
- Online loop closure with Gaussian map correction — consistent large-scale reconstructions
WHY THIS IS HARD
Fusing multi-sensor streams into real-time Gaussian SLAM requires solving tightly coupled problems:
- 01Sensor synchronization: LiDAR, camera, and IMU run at different rates — temporal alignment must be sub-millisecond to avoid ghosting artifacts in the Gaussian map
- 02Incremental Gaussian initialization: each new LiDAR scan must seed Gaussian splats at correct 3D positions while avoiding redundancy with existing map elements
- 03Joint optimization: trajectory estimation and Gaussian parameters must be co-optimized — errors in pose propagate into map distortion and vice versa
- 04Real-time constraint: the full pipeline — sensor fusion, trajectory update, Gaussian refinement, rendering — must complete within the LiDAR scan period (~100ms)
- 05Mesh extraction: converting a continuous Gaussian field to a watertight mesh requires marching cubes on the implicit density field with careful threshold selection
PRISM solves these by tight-coupling LiDAR geometric constraints with camera photometric gradients through a shared Gaussian representation, enabling both real-time SLAM and photorealistic rendering from the same data structure.
PROOF, NOT PROMISES
Target performance metrics for production deployment:
| METRIC | VALUE |
|---|---|
| SLAM Mode | Real-time (10 Hz LiDAR) |
| Sensor Fusion | LiDAR + Camera + IMU |
| Trajectory Accuracy | <2cm drift / 100m |
| Map Representation | Gaussian Splats |
| Rendering Quality | Photorealistic RGB + Depth |
| Mesh Export | Watertight geometry |
| Map Update Rate | 10 Hz incremental |
| Splat Count (typical) | 500K–2M per scene |
| Rendering Resolution | 1920×1080 @ 30 FPS |
| GPU Requirement | NVIDIA RTX 3080+ |
| Loop Closure | Online, Gaussian-corrected |
| ANIMA Integration | GENESIS + SYNTHESIS |
WHAT'S BUILT TODAY
| COMPONENT | STATUS | NOTES |
|---|---|---|
| Core models | COMPLETE | Gaussian SLAM pipeline trained and validated |
| Trajectory Estimation | COMPLETE | Continuous-time trajectory with IMU pre-integration |
| Gaussian Map Builder | COMPLETE | Incremental splat initialization and refinement |
| RGB/Depth Rendering | IN PROGRESS | Differentiable rendering — optimizing for real-time |
| Mesh Export | COMPLETE | Marching cubes on Gaussian density field |
| Multi-Sensor Fusion | IN PROGRESS | LiDAR+Camera tight coupling — IMU integration ongoing |
| API layer | IN PROGRESS | REST endpoints for map query and export |
| Loop Closure | PLANNED | Architecture designed — pending integration |
| Docker Deployment | PLANNED | GPU container with CUDA + rendering stack |
WHERE PRISM DEPLOYS
- APP_01
AUTONOMOUS NAVIGATION
Real-time photorealistic mapping for mobile robots — dense 3D understanding beyond sparse occupancy grids.
- APP_02
DIGITAL TWINS
Generate photorealistic 3D reconstructions of physical spaces for simulation, planning, and remote inspection.
- APP_03
ROBOTIC MANIPULATION
Dense scene maps enable precise object localization and grasp planning with full geometry and appearance.
- APP_04
CONSTRUCTION & INSPECTION
Continuous site mapping with centimeter-level accuracy — exportable meshes for BIM integration.
- APP_05
SIM-TO-REAL TRANSFER
Gaussian maps serve as photorealistic simulation environments for training manipulation policies.
- APP_06
MULTI-ROBOT MAPPING
Federated Gaussian maps from multiple robots merged into a single coherent global reconstruction.
UNDER THE HOOD
FOUNDATION: GAUSSIAN SPLATTING + SLAM
- 3D Gaussian Splatting for photorealistic novel-view synthesis
- Continuous-time trajectory estimation with B-spline parameterization
- LiDAR-initialized Gaussian splats with camera-refined appearance
- Incremental map construction — no offline batch processing required
KEY INNOVATION
Tight coupling of multi-sensor SLAM with incremental Gaussian Splatting — LiDAR provides geometric anchors for splat initialization, cameras refine appearance through differentiable rendering, and IMU constrains the continuous trajectory. The result is a single map representation that supports both real-time localization and photorealistic rendering.
PIPELINE STAGES
- Sensor sync → IMU pre-integration → trajectory update
- LiDAR scan → splat initialization → geometric refinement
- Camera frame → photometric optimization → appearance update
- Periodic mesh extraction via marching cubes on Gaussian density
SENSOR STACK
- LiDAR
- Ouster/Velodyne 64-128 channel — geometric backbone at 10-20 Hz
- Camera
- Stereo or monocular RGB — photometric refinement at 30 Hz
- IMU
- 6-axis inertial — motion prior at 200-400 Hz
RESEARCH PAPERS
- [01]3D Gaussian Splatting for Real-Time Radiance Field Rendering (Kerbl et al., 2023)
- [02]LiDAR-Camera Fusion for Dense 3D Reconstruction
- [03]Continuous-Time SLAM with B-Spline Trajectories