Kimera-Multi: a System for Distributed Multi-Robot Metric-Semantic Simultaneous Localization and Mapping
arXiv:2011.04087 · doi:10.1109/ICRA48506.2021.9561090
Abstract
We present the first fully distributed multi-robot system for dense metric-semantic Simultaneous Localization and Mapping (SLAM). Our system, dubbed Kimera-Multi, is implemented by a team of robots equipped with visual-inertial sensors, and builds a 3D mesh model of the environment in real-time, where each face of the mesh is annotated with a semantic label (e.g., building, road, objects). In Kimera-Multi, each robot builds a local trajectory estimate and a local mesh using Kimera. Then, when two robots are within communication range, they initiate a distributed place recognition and robust pose graph optimization protocol with a novel incremental maximum clique outlier rejection; the protocol allows the robots to improve their local trajectory estimates by leveraging inter-robot loop closures. Finally, each robot uses its improved trajectory estimate to correct the local mesh using mesh deformation techniques. We demonstrate Kimera-Multi in photo-realistic simulations and real data. Kimera-Multi (i) is able to build accurate 3D metric-semantic meshes, (ii) is robust to incorrect loop closures while requiring less computation than state-of-the-art distributed SLAM back-ends, and (iii) is efficient, both in terms of computation at each robot as well as communication bandwidth.
arXiv comment9 pages
References in corpus (13)
- Open3D: A Modern Library for 3D Data Processing
- A Review on Deep Learning Techniques Applied to Semantic Segmentation
- Graduated Non-Convexity for Robust Spatial Perception: From Non-Minimal Solvers to Global Outlier Rejection
- DOOR-SLAM: Distributed, Online, and Outlier Resilient SLAM for Robotic Teams
- Co-Fusion: Real-time Segmentation, Tracking and Fusion of Multiple Objects
- Volumetric Instance-Aware Semantic Mapping and 3D Object Discovery
- Fast Algorithms for the Maximum Clique Problem on Massive Graphs with Applications to Overlapping Community Detection
- Asynchronous and Parallel Distributed Pose Graph Optimization
- FutureMapping: The Computational Structure of Spatial AI Systems
- Active Rendezvous for Multi-Robot Pose Graph Optimization using Sensing over Wi-Fi
- Distributed Consistent Multi-Robot Semantic Localization and Mapping
- Distributed Certifiably Correct Pose-Graph Optimization
- GeoD: Consensus-based Geodesic Distributed Pose Graph Optimization