research-article

Bio-inspired monocular drone SLAM

Authors:

Matthias Hartmann,

Bart DhoedtAuthors Info & Claims

DroneSE and RAPIDO: System Engineering for constrained embedded systems

Pages 21 - 26

https://doi.org/10.1145/3522784.3522788

Published: 23 June 2022 Publication History

Abstract

Drone navigation in GPS-denied, indoor environments, is still a challenging problem. As drones can perceive the environment from a richer set of viewpoints, simultaneous localization and mapping (SLAM) becomes more complex, while having stringent compute and energy constraints. To tackle that problem, this research displays a biologically inspired deep-learning algorithm for monocular SLAM on a drone platform. We propose an unsupervised representation learning method that yields low-dimensional latent state descriptors, that mitigates the sensitivity to perceptual aliasing, and works on power-efficient, embedded hardware. We compare our method against ORB-SLAM3, and showcase increased robustness and an order of magnitude lower memory overhead.

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Cited By

Uwano F(2024)Learning Agents in Robot Navigation: Trends and Next ChallengesJournal of Robotics and Mechatronics10.20965/jrm.2024.p050836:3(508-516)Online publication date: 20-Jun-2024
https://doi.org/10.20965/jrm.2024.p0508
Safa AKeuninckx LGielen GCatthoor FSafa AKeuninckx LGielen GCatthoor F(2024)Sensor Fusion SLAM with Continual STDP LearningNeuromorphic Solutions for Sensor Fusion and Continual Learning Systems10.1007/978-3-031-63565-6_5(125-153)Online publication date: 4-Jun-2024
https://doi.org/10.1007/978-3-031-63565-6_5
Sunil SMozaffari SSingh RShahrrava BAlirezaee S(2023)Feature-Based Occupancy Map-Merging for Collaborative SLAMSensors10.3390/s2306311423:6(3114)Online publication date: 14-Mar-2023
https://doi.org/10.3390/s23063114

Bio-inspired monocular drone SLAM
1. Computer systems organization
  1. Embedded and cyber-physical systems

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cover image ACM Other conferences

DroneSE and RAPIDO: System Engineering for constrained embedded systems

January 2022

58 pages

ISBN:9781450395663

DOI:10.1145/3522784

Copyright © 2022 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 June 2022

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DroneSE and RAPIDO '22

DroneSE and RAPIDO '22: System Engineering for constrained embedded systems

January 17 - 19, 2022

Budapest, Hungary

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Cited By

Uwano F(2024)Learning Agents in Robot Navigation: Trends and Next ChallengesJournal of Robotics and Mechatronics10.20965/jrm.2024.p050836:3(508-516)Online publication date: 20-Jun-2024
https://doi.org/10.20965/jrm.2024.p0508
Safa AKeuninckx LGielen GCatthoor FSafa AKeuninckx LGielen GCatthoor F(2024)Sensor Fusion SLAM with Continual STDP LearningNeuromorphic Solutions for Sensor Fusion and Continual Learning Systems10.1007/978-3-031-63565-6_5(125-153)Online publication date: 4-Jun-2024
https://doi.org/10.1007/978-3-031-63565-6_5
Sunil SMozaffari SSingh RShahrrava BAlirezaee S(2023)Feature-Based Occupancy Map-Merging for Collaborative SLAMSensors10.3390/s2306311423:6(3114)Online publication date: 14-Mar-2023
https://doi.org/10.3390/s23063114

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