Robust Legged Robot State Estimation Using Factor Graph Optimization

Abstract

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.

8 pages, 12 figures. Accepted to RA-L + IROS 2019, July 2019

References in corpus (3)

• VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator
• On-Manifold Preintegration for Real-Time Visual-Inertial Odometry
• Fast and Continuous Foothold Adaptation for Dynamic Locomotion through CNNs

Cited by in corpus (10)

• VILENS: Visual, Inertial, Lidar, and Leg Odometry for All-Terrain Legged Robots
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• Online Tree Reconstruction and Forest Inventory on a Mobile Robotic System
• Tightly-Coupled LiDAR-IMU-Leg Odometry with Online Learned Leg Kinematics Incorporating Foot Tactile Information
• Continuous-time State & Dynamics Estimation using a Pseudo-Spectral Parameterization
• A New Tightly-Coupled Dual-VIO for a Mobile Manipulator With Dynamic Locomotion
• Line-Circle-Square (LCS): A Multilayered Geometric Filter for Edge-Based Detection
• RigidFusion: Robot Localisation and Mapping in Environments with Large Dynamic Rigid Objects
• Legged Robot State Estimation Using Invariant Neural-Augmented Kalman Filter with a Neural Compensator