Header

UZH-Logo

Maintenance Infos

Redesigning SLAM for Arbitrary Multi-Camera Systems


Kuo, Juichung; Muglikar, Manasi; Zhang, Zichao; Scaramuzza, Davide (2020). Redesigning SLAM for Arbitrary Multi-Camera Systems. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France, 1 July 2020 - 1 October 2020. IEEE, 2116-2122.

Abstract

Adding more cameras to SLAM systems improves robustness and accuracy but complicates the design of the visual front-end significantly. Thus, most systems in the literature are tailored for specific camera configurations. In this work, we aim at an adaptive SLAM system that works for arbitrary multi-camera setups. To this end, we revisit several common building blocks in visual SLAM. In particular, we propose an adaptive initialization scheme, a sensor-agnostic, information- theoretic keyframe selection algorithm, and a scalable voxel- based map. These techniques make little assumption about the actual camera setups and prefer theoretically grounded methods over heuristics. We adapt a state-of-the-art visual- inertial odometry with these modifications, and experimental results show that the modified pipeline can adapt to a wide range of camera setups (e.g., 2 to 6 cameras in one experiment) without the need of sensor-specific modifications or tuning.

Abstract

Adding more cameras to SLAM systems improves robustness and accuracy but complicates the design of the visual front-end significantly. Thus, most systems in the literature are tailored for specific camera configurations. In this work, we aim at an adaptive SLAM system that works for arbitrary multi-camera setups. To this end, we revisit several common building blocks in visual SLAM. In particular, we propose an adaptive initialization scheme, a sensor-agnostic, information- theoretic keyframe selection algorithm, and a scalable voxel- based map. These techniques make little assumption about the actual camera setups and prefer theoretically grounded methods over heuristics. We adapt a state-of-the-art visual- inertial odometry with these modifications, and experimental results show that the modified pipeline can adapt to a wide range of camera setups (e.g., 2 to 6 cameras in one experiment) without the need of sensor-specific modifications or tuning.

Statistics

Citations

Dimensions.ai Metrics
25 citations in Web of Science®
27 citations in Scopus®
Google Scholar™

Altmetrics

Downloads

95 downloads since deposited on 17 Dec 2020
29 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Scopus Subject Areas:Physical Sciences > Software
Physical Sciences > Control and Systems Engineering
Physical Sciences > Artificial Intelligence
Physical Sciences > Electrical and Electronic Engineering
Scope:Discipline-based scholarship (basic research)
Language:English
Event End Date:1 October 2020
Deposited On:17 Dec 2020 08:57
Last Modified:06 Mar 2024 14:33
Publisher:IEEE
ISBN:978-1-7281-7395-5
OA Status:Green
Publisher DOI:https://doi.org/10.1109/icra40945.2020.9197553
Other Identification Number:merlin-id:20307
  • Content: Accepted Version