Infrastructure-Aided Localization and State Estimation for Autonomous Mobile Robots
A slip-aware localization framework is proposed for mobile robots experiencing wheel slip in dynamic environments. The framework fuses infrastructure-aided visual tracking data (via fisheye lenses) and proprioceptive sensory data from a skid-steer mobile robot to enhance accuracy and reduce variance...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-08-01
|
| Series: | Robotics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2218-6581/11/4/82 |
| _version_ | 1797408122347716608 |
|---|---|
| author | Daniel Flögel Neel Pratik Bhatt Ehsan Hashemi |
| author_facet | Daniel Flögel Neel Pratik Bhatt Ehsan Hashemi |
| author_sort | Daniel Flögel |
| collection | DOAJ |
| description | A slip-aware localization framework is proposed for mobile robots experiencing wheel slip in dynamic environments. The framework fuses infrastructure-aided visual tracking data (via fisheye lenses) and proprioceptive sensory data from a skid-steer mobile robot to enhance accuracy and reduce variance of the estimated states. The slip-aware localization framework includes: the visual thread to detect and track the robot in the stereo image through computationally efficient 3D point cloud generation using a region of interest; and the ego motion thread which uses a slip-aware odometry mechanism to estimate the robot pose utilizing a motion model considering wheel slip. Covariance intersection is used to fuse the pose prediction (using proprioceptive data) and the visual thread, such that the updated estimate remains consistent. As confirmed by experiments on a skid-steer mobile robot, the designed localization framework addresses state estimation challenges for indoor/outdoor autonomous mobile robots which experience high-slip, uneven torque distribution at each wheel (by the motion planner), or occlusion when observed by an infrastructure-mounted camera. The proposed system is real-time capable and scalable to multiple robots and multiple environmental cameras. |
| first_indexed | 2024-03-09T03:53:46Z |
| format | Article |
| id | doaj.art-bd73d161e0b44d8785237cb5442b8f98 |
| institution | Directory Open Access Journal |
| issn | 2218-6581 |
| language | English |
| last_indexed | 2024-03-09T03:53:46Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Robotics |
| spelling | doaj.art-bd73d161e0b44d8785237cb5442b8f982023-12-03T14:24:05ZengMDPI AGRobotics2218-65812022-08-011148210.3390/robotics11040082Infrastructure-Aided Localization and State Estimation for Autonomous Mobile RobotsDaniel Flögel0Neel Pratik Bhatt1Ehsan Hashemi2Institute for Regulation and Control Systems, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, GermanyMechanical and Mechatronics Engineering Department, University of Waterloo, 200 University Ave W, Waterloo, ON N2L 3G1, CanadaMechanical Engineering Department, University of Alberta, 9211-116 Street NW, Edmonton, AB T6G 1H9, CanadaA slip-aware localization framework is proposed for mobile robots experiencing wheel slip in dynamic environments. The framework fuses infrastructure-aided visual tracking data (via fisheye lenses) and proprioceptive sensory data from a skid-steer mobile robot to enhance accuracy and reduce variance of the estimated states. The slip-aware localization framework includes: the visual thread to detect and track the robot in the stereo image through computationally efficient 3D point cloud generation using a region of interest; and the ego motion thread which uses a slip-aware odometry mechanism to estimate the robot pose utilizing a motion model considering wheel slip. Covariance intersection is used to fuse the pose prediction (using proprioceptive data) and the visual thread, such that the updated estimate remains consistent. As confirmed by experiments on a skid-steer mobile robot, the designed localization framework addresses state estimation challenges for indoor/outdoor autonomous mobile robots which experience high-slip, uneven torque distribution at each wheel (by the motion planner), or occlusion when observed by an infrastructure-mounted camera. The proposed system is real-time capable and scalable to multiple robots and multiple environmental cameras.https://www.mdpi.com/2218-6581/11/4/82indoor localizationstate estimationcovariance intersectionuncertainty-aware state observer |
| spellingShingle | Daniel Flögel Neel Pratik Bhatt Ehsan Hashemi Infrastructure-Aided Localization and State Estimation for Autonomous Mobile Robots Robotics indoor localization state estimation covariance intersection uncertainty-aware state observer |
| title | Infrastructure-Aided Localization and State Estimation for Autonomous Mobile Robots |
| title_full | Infrastructure-Aided Localization and State Estimation for Autonomous Mobile Robots |
| title_fullStr | Infrastructure-Aided Localization and State Estimation for Autonomous Mobile Robots |
| title_full_unstemmed | Infrastructure-Aided Localization and State Estimation for Autonomous Mobile Robots |
| title_short | Infrastructure-Aided Localization and State Estimation for Autonomous Mobile Robots |
| title_sort | infrastructure aided localization and state estimation for autonomous mobile robots |
| topic | indoor localization state estimation covariance intersection uncertainty-aware state observer |
| url | https://www.mdpi.com/2218-6581/11/4/82 |
| work_keys_str_mv | AT danielflogel infrastructureaidedlocalizationandstateestimationforautonomousmobilerobots AT neelpratikbhatt infrastructureaidedlocalizationandstateestimationforautonomousmobilerobots AT ehsanhashemi infrastructureaidedlocalizationandstateestimationforautonomousmobilerobots |