Abstract
Robust and accurate trajectory estimation of mobile agents such as people and robots is a key requirement for providing spatial awareness for emerging capabilities such as augmented reality or autonomous interaction. Although currently dominated by optical techniques e.g., visual-inertial odometry these suffer from challenges with scene illumination or featureless surfaces. As an alternative, we propose milliEgo, a novel deep-learning approach to robust egomotion estimation which exploits the capabilities of low-cost mm Wave radar. Although mmWave radar has a fundamental advantage over monocular cameras of being metric i.e., providing absolute scale or depth, current single chip solutions have limited and sparse imaging resolution, making existing point-cloud registration techniques brittle. We propose a new architecture that is optimized for solving this challenging pose transformation problem. Secondly, to robustly fuse mmWave pose estimates with additional sensors, e.g. inertial or visual sensors we introduce a mixed attention approach to deep fusion. Through extensive experiments, we demonstrate our proposed system is able to achieve 1.3% 3D error drift and generalizes well to unseen environments. We also show that the neural architecture can be made highly efficient and suitable for real-time embedded applications.
Original language | English |
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Title of host publication | Proceedings of the 18th ACM Conference on Embedded Networked Sensor Systems |
Editors | Shunsuke Saruwatari |
Place of Publication | New York NY USA |
Publisher | Association for Computing Machinery (ACM) |
Pages | 109-122 |
Number of pages | 14 |
ISBN (Electronic) | 9781450375900 |
DOIs | |
Publication status | Published - 2020 |
Externally published | Yes |
Event | ACM Conference on Embedded Networked Sensor Systems 2020 - Online, Japan Duration: 16 Nov 2020 → 19 Nov 2020 Conference number: 18th https://dl-acm-org.ezproxy.lib.monash.edu.au/doi/proceedings/10.1145/3384419 (Proceedings) |
Publication series
Name | SenSys 2020 - Proceedings of the 2020 18th ACM Conference on Embedded Networked Sensor Systems |
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Conference
Conference | ACM Conference on Embedded Networked Sensor Systems 2020 |
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Abbreviated title | SenSys 2020 |
Country/Territory | Japan |
Period | 16/11/20 → 19/11/20 |
Internet address |
Keywords
- egomotion estimation
- indoor localization
- millimeter wave radar