OutlierRobust State Estimation for Humanoid Robots * Contemporary humanoids are equipped with visual and LiDAR sensors that are effectively utilized for Visual Odometry (VO) and LiDAR Odometry (LO). Unfortunately, such measurements commonly suffer from outliers in a dynamic environment, since frequently it is assumed that only the robot is in motion and the world is static. To this end, robust state estimation schemes are mandatory in order for humanoids to symbiotically coexist with humans in their daily dynamic environments. In this article, the robust Gaussian ErrorState Kalman Filter for humanoid robot locomotion is presented. The introduced method automatically detects and rejects outliers without relying on any prior knowledge on measurement distributions or finely tuned thresholds. Subsequently, the proposed method is quantitatively and qualitatively assessed in realistic conditions with the fullsize humanoid robot WALKMAN v2.0 and the minisize humanoid robot NAO to demonstrate its accuracy and robustness when outlier VOLO measurements are present. Finally, in order to reinforce further research endeavours, our implementation is released as an opensource ROSCpackage.

A Robust Position Estimation Algorithm under Unusual Large Range Errors This paper provides a robust fusion algorithm for accurate position estimation under uncertain large errors in range measurements. Many researchers have studied UWB, IMU, odometry integration algorithms for estimating position, velocity, attitude and IMU biases. However, the fused results of these conventional algorithms are likely to be affected by the unusual ranging measurement errors. Therefore, in order to improve the positioning accuracy under the large errors of the UWB range, a robust fusion algorithm is required. In this paper, instead of using the range directly, the estimated range that the errors is reduced is used. Reducing the errors is possible by using the odometry velocity that is relatively accurate and is independent of range measurement. The robustness and accuracy of the proposed algorithm is verified by a mobile robot with realtime positioning and trajectory control under large range errors which are occurred randomly.