Vijayakumar, Sethu

Aodha, Oisin Mac

Long, Ran

2023-08-29T15:56:38Z

2023-08-29T15:56:38Z

2023-08-29

Visual sensors are key to robot perception, which can not only help robot localisation but also enable robots to interact with the environment. However, in new environments, robots can fail to distinguish the static and dynamic components in the visual input. Consequently, robots are unable to track objects or localise themselves. Methods often require precise robot proprioception to compensate for camera movement and separate the static background from the visual input. However, robot proprioception, such as \ac{IMU} or wheel odometry, usually faces the problem of drift accumulation. The state-of-the-art methods demonstrate promising performance but either (1) require semantic segmentation, which is inaccessible in unknown environments, or (2) treat dynamic components as outliers -- which is unfeasible when dynamic objects occupy a large proportion of the visual input. This research work systematically unifies camera and multi-object tracking problems in indoor environments by proposing a multi-motion tracking system; and enables robots to differentiate the static and dynamic components in the visual input with the understanding of their own movements and actions. Detailed evaluation of both simulation environments and robotic platforms suggests that the proposed method outperforms the state-of-the-art dynamic SLAM methods when the majority of the camera view is occluded by multiple unmodeled objects over a long period of time.

https://hdl.handle.net/1842/40894

http://dx.doi.org/10.7488/era/3647

en

The University of Edinburgh

Ran Long, Christian Rauch, Tianwei Zhang, Vladimir Ivan and Sethu Vijayakumar, ‘RigidFusion: Robot Localisation and Mapping in Environments With Large Dynamic Rigid Objects’, in IEEE Robotics and Automation Letters (RA-L), vol. 6, no. 2, pp. 3703-3710, April 2021. (Presented at: IEEE International Conference on Robotics and Automation (ICRA 2021))

Ran Long, Christian Rauch, Tianwei Zhang, Vladimir Ivan, Tin Lun Lam and Sethu Vijayakumar, ‘RGB-D SLAM in Indoor Planar Environments With Multiple Large Dynamic Objects’, in IEEE Robotics and Automation Letters (RA-L), vol. 7, no. 3, pp. 8209-8216, July 2022. (Presented at: IEEE/RSJ International. Conference on Intelligent Robots and Systems (IROS 2022)) (Chapter 5)

Ran Long, Christian Rauch, Tianwei Zhang, Vladimir Ivan, Tin Lun Lam and Sethu Vijayakumar, ‘RGB-D-Inertial SLAM in Indoor Dynamic Environments with Long-term Large Occlusion’. arXiv preprint arXiv:2303.13316 (2023). (Chapter 6) vii

Christian Rauch, Ran Long, Vladimir Ivan and Sethu Vijayakumar, ‘Sparse- Dense Motion Modelling and Tracking for Manipulation Without Prior Object Models’, in IEEE Robotics and Automation Letters (RA-L), vol. 7, no. 4, pp. 11394-11401, Oct. 2022. (Presented at: IEEE International Conference on Robotics and Automation (ICRA 2023))(Chapter 4)

SLAM

Sensor Fusion

Object Tracking

Robust SLAM and motion segmentation under long-term dynamic large occlusions

Thesis or Dissertation

Doctoral

PhD Doctor of Philosophy