Robust Legged Robot State Estimation Using Factor Graph Optimization

Legged robots, specifically quadrupeds, are becoming increasingly attractive for industrial applications such as inspection. However, to leave the laboratory and to become useful to an end user requires reliability in harsh conditions. From the perspective of state estimation, it is essential to be able to accurately estimate the robot's state despite challenges such as uneven or slippery terrain, textureless and reflective scenes, as well as dynamic camera occlusions. We are motivated to reduce the dependency on foot contact classifications, which fail when slipping, and to reduce position drift during dynamic motions such as trotting. To this end, we present a factor graph optimization method for state estimation which tightly fuses and smooths inertial navigation, leg odometry and visual odometry. The effectiveness of the approach is demonstrated using the ANYmal quadruped robot navigating in a realistic outdoor industrial environment. This experiment included trotting, walking, crossing obstacles and ascending a staircase. The proposed approach decreased the relative position error by up to 55% and absolute position error by 76% compared to kinematic-inertial odometry.Comment: 8 pages, 12 figures. Accepted to RA-L + IROS 2019, July 201

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

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.Comment: Accepted to ICRA 2020. Video: youtu.be/w1Sx6dIqgQ