- Title
- Monocular ORB-SLAM on a humanoid robot for localization purposes
- Creator
- Ginn, Daniel; Mendes, Alexandre; Chalup, Stephan; Fountain, Jake
- Relation
- 31st Australasian Joint Conference (AI 2018). AI 2018: Advances in Artificial Intelligence: 31st Australasian Joint Conference, Wellington, New Zealand, December 11-14, 2018, Proceedings [presented in Lecture Notes in Artifical Intelligence, Vol. 11320] (Wellington, New Zealand 11-14 December, 2018) p. 77-82
- Publisher Link
- http://dx.doi.org/10.1007/978-3-030-03991-2_8
- Publisher
- Springer Nature
- Resource Type
- conference paper
- Date
- 2018
- Description
- This work presents an application of ORB-SLAM in an iGus bipedal humanoid robotic platform. The method was adapted from its original implementation into the framework used by the NUbots robotic soccer team and used for localization purposes. The paper presents a description of the challenges to implement the adaptation, as well as several tests where the method's performance is analyzed to determine its suitability for further development and use on medium sized humanoid robots. To conduct the tests, we determined the robot's real location using a high-accuracy, camera-based infrared tracking system. Two experiments were performed to estimate the robustness of the method to the vibration and constant camera wobbling inherent to a bipedal walk and its ability to deal with the kidnapped robot problem. The tests indicate that ORB-SLAM is suitable for implementation into a medium sized humanoid robot in situations comparable to a robotic soccer environment, and requires relatively low computational resources, leaving enough CPU power for other tasks. Additionally, since ORB-SLAM is robust to the difficulties associated with humanoid motion, we conclude that it provides a good SLAM algorithm to enhance with features specific to the humanoid robotic platform.
- Subject
- ORB-SLAM; humanoid; visual odometry; visual SLAM
- Identifier
- http://hdl.handle.net/1959.13/1408888
- Identifier
- uon:35899
- Identifier
- ISBN:9783030039905
- Language
- eng
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