Conference item
Haptic sequential Monte Carlo localization for quadrupedal locomotion in vision-denied scenarios
- Abstract:
- Continuous robot operation in extreme scenarios such as underground mines or sewers is difficult because exteroceptive sensors may fail due to fog, darkness, dirt or malfunction. So as to enable autonomous navigation in these kinds of situations, we have developed a type of proprioceptive localization which exploits the foot contacts made by a quadruped robot to localize against a prior map of an environment, without the help of any camera or LIDAR sensor. The proposed method enables the robot to accurately re-localize itself after making a sequence of contact events over a terrain feature. The method is based on Sequential Monte Carlo and can support both 2.5D and 3D prior map representations. We have tested the approach online and onboard the ANYmal quadruped robot in two different scenarios: the traversal of a custom built wooden terrain course and a wall probing and following task. In both scenarios, the robot is able to effectively achieve a localization match and to execute a desired pre-planned path. The method keeps the localization error down to 10 cm on feature rich terrain by only using its feet, kinematic and inertial sensing.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
-
-
(Preview, Accepted manuscript, pdf, 4.4MB, Terms of use)
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- Publisher copy:
- 10.1109/IROS45743.2020.9341128
- Publication website:
- https://ieeexplore.ieee.org/document/9341128/
Authors
- Publisher:
- IEEE
- Host title:
- 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- Pages:
- 3657-3663
- Publication date:
- 2021-02-10
- Acceptance date:
- 2020-06-30
- Event title:
- 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- Event location:
- Online
- Event website:
- https://www.iros2020.org/index.html
- Event start date:
- 2020-10-25
- Event end date:
- 2021-01-24
- DOI:
- EISSN:
-
2153-0866
- ISSN:
-
2153-0858
- EISBN:
- 978-1-7281-6212-6
- ISBN:
- 978-1-7281-6213-3
- Language:
-
English
- Keywords:
- Pubs id:
-
1165800
- Local pid:
-
pubs:1165800
- Deposit date:
-
2021-03-03
- ARK identifier:
Terms of use
- Copyright holder:
- IEEE
- Copyright date:
- 2020
- Rights statement:
- ©2020 IEEE
- Notes:
- This is the accepted manuscript version of a paper presented online at the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 24 Oct 2020 to 24 Jan 2021. The final published version is available from IEEE at https://doi.org/10.1109/IROS45743.2020.9341128
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