VILENS: Visual, Inertial, Lidar, and Leg Odometry for All-Terrain Legged Robots
arXiv:2107.07243 · doi:10.1109/TRO.2022.3193788
Abstract
We present visual inertial lidar legged navigation system (VILENS), an odometry system for legged robots based on factor graphs. The key novelty is the tight fusion of four different sensor modalities to achieve reliable operation when the individual sensors would otherwise produce degenerate estimation. To minimize leg odometry drift, we extend the robot's state with a linear velocity bias term, which is estimated online. This bias is observable because of the tight fusion of this preintegrated velocity factor with vision, lidar, and inertial measurement unit (IMU) factors. Extensive experimental validation on different ANYmal quadruped robots is presented, for a total duration of 2 h and 1.8 km traveled. The experiments involved dynamic locomotion over loose rocks, slopes, and mud, which caused challenges such as slippage and terrain deformation. Perceptual challenges included dark and dusty underground caverns, and open and feature-deprived areas. We show an average improvement of 62% translational and 51% rotational errors compared to a state-of-the-art loosely coupled approach. To demonstrate its robustness, VILENS was also integrated with a perceptive controller and a local path planner.
Video: https://youtu.be/NG4pkjJKhus
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- COIN-LIO: Complementary Intensity-Augmented LiDAR Inertial Odometry
- GLIM: 3D Range-Inertial Localization and Mapping with GPU-Accelerated Scan Matching Factors
- GNSS/Multi-Sensor Fusion Using Continuous-Time Factor Graph Optimization for Robust Localization
- Balancing the Budget: Feature Selection and Tracking for Multi-Camera Visual-Inertial Odometry
- Factor Graph Fusion of Raw GNSS Sensing with IMU and Lidar for Precise Robot Localization without a Base Station
- SiLVR: Scalable Lidar-Visual Reconstruction with Neural Radiance Fields for Robotic Inspection
- Observability-aware online multi-lidar extrinsic calibration
- Osprey: Multi-Session Autonomous Aerial Mapping with LiDAR-based SLAM and Next Best View Planning
- Multi-Sensor Fusion for Quadruped Robot State Estimation using Invariant Filtering and Smoothing
- FR-Net: Learning Robust Quadrupedal Fall Recovery on Challenging Terrains through Mass-Contact Prediction
- Tightly-Coupled LiDAR-IMU-Leg Odometry with Online Learned Leg Kinematics Incorporating Foot Tactile Information
- AutoInspect: Towards Long-Term Autonomous Industrial Inspection
- M-LIO: Multi-lidar, multi-IMU odometry with sensor dropout tolerance
- 3D Freehand Ultrasound using Visual Inertial and Deep Inertial Odometry for Measuring Patellar Tracking
- LVI-Q: Robust LiDAR-Visual-Inertial-Kinematic Odometry for Quadruped Robots Using Tightly-Coupled and Efficient Alternating Optimization
- Probabilistic approach to feedback control enhances multi-legged locomotion on rugged landscapes
- Robust Reinforcement Learning-Based Locomotion for Resource-Constrained Quadrupeds with Exteroceptive Sensing