Preintegrated Velocity Bias Estimation to Overcome Contact Nonlinearities in Legged Robot Odometry

Abstract

In this paper, we present a novel factor graph formulation to estimate the pose and velocity of a quadruped robot on slippery and deformable terrain. The factor graph introduces a preintegrated velocity factor that incorporates velocity inputs from leg odometry and also estimates related biases. From our experimentation we have seen that it is difficult to model uncertainties at the contact point such as slip or deforming terrain, as well as leg flexibility. To accommodate for these effects and to minimize leg odometry drift, we extend the robot's state vector with a bias term for this preintegrated velocity factor. The bias term can be accurately estimated thanks to the tight fusion of the preintegrated velocity factor with stereo vision and IMU factors, without which it would be unobservable. The system has been validated on several scenarios that involve dynamic motions of the ANYmal robot on loose rocks, slopes and muddy ground. We demonstrate a 26% improvement of relative pose error compared to our previous work and 52% compared to a state-of-the-art proprioceptive state estimator.

arXiv commentAccepted to ICRA 2020. Video: youtu.be/w1Sx6dIqgQo

References in corpus (3)

• On-Manifold Preintegration for Real-Time Visual-Inertial Odometry
• Robust Legged Robot State Estimation Using Factor Graph Optimization
• Hybrid Contact Preintegration for Visual-Inertial-Contact State Estimation Using Factor Graphs

Cited by in corpus (4)

• VILENS: Visual, Inertial, Lidar, and Leg Odometry for All-Terrain Legged Robots
• Unified Multi-Modal Landmark Tracking for Tightly Coupled Lidar-Visual-Inertial Odometry
• Learning Inertial Odometry for Dynamic Legged Robot State Estimation
• Line-Circle-Square (LCS): A Multilayered Geometric Filter for Edge-Based Detection