Outlier-Robust State Estimation for Humanoid Robots

Stylianos Piperakis1, Dimitrios Kanoulas2, Nikos Tsagarakis3, Panos Trahanias4

  • 1Foundation for Research and Technology – Hellas (FORTH)
  • 2University College London
  • 3Istituto Italiano di Tecnologia
  • 4FORTH

Details

11:30 - 11:45 | Tue 5 Nov | LG-R18 | TuAT18.3

Session: Localization I

Abstract

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 co-exist with humans in their daily dynamic environments. In this article, the robust Gaussian Error-State 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 full-size humanoid robot WALK-MAN v2.0 and the mini-size humanoid robot NAO to demonstrate its accuracy and robustness when outlier VO/LO measurements are present. Finally, in order to reinforce further research endeavours, our implementation is released as an open-source ROS/C++ package.