Name
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 VOLO measurements are present. Finally, in order to reinforce further research endeavours, our implementation is released as an open-source ROS/C++package. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/IROS40897.2019.8968152 | 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) |
Keywords | Field | DocType |
open-source ROS-C++package,robust Gaussian error-state kalman filter,LiDAR odometry,visual odometry,outlier-robust State estimation,outlier VOLO measurements,mini-size humanoid robot NAO,full-size humanoid robot WALK-MAN,measurement distributions,humanoid robot locomotion,robust state estimation schemes,dynamic environment,visual LiDAR sensors | Computer vision,Visual odometry,Computer science,Odometry,Outlier,Kalman filter,Robustness (computer science),Gaussian,Artificial intelligence,Robot,Humanoid robot | Conference |
ISSN | ISBN | Citations |
2153-0858 | 978-1-7281-4005-6 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stylianos Piperakis | 1 | 5 | 3.15 |
| Dimitrios Kanoulas | 2 | 3 | 3.42 |
| NG Tsagarakis | 3 | 1492 | 159.09 |
| Panos Trahanias | 4 | 19 | 3.95 |