ファクターグラフ最適化による堅牢な脚式ロボットの状態推定
Robust Legged Robot State Estimation Using Factor Graph Optimization
慣性・脚オドメトリ・視覚オドメトリをファクターグラフで融合し、滑りや動的動作に強い四足ロボットの状態推定手法を提案、屋外産業環境で精度向上を実証した。
著者: David Wisth, Marco Camurri, Maurice Fallon
分類: cs.RO
原文アブストラクト
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.
