TY - GEN
T1 - Benchmarking Visual Localization for Autonomous Navigation
AU - Suomela, Lauri
AU - Kalliola, Jussi
AU - Dag, Atakan
AU - Edelman, Harry
AU - Kämäräinen, Joni-Kristian
N1 - Funding Information:
Acknowledgements. This research has received funding from the Technology Innovation Institute (TII) as part of the ARROWSMITH project.
Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - This work introduces a simulator-based benchmark for visual localization in the autonomous navigation context. The dynamic benchmark enables investigation of how variables such as the time of day, weather, and camera perspective affect the navigation performance of autonomous agents that utilize visual localization for closed-loop control. The experimental part of the paper studies the effects of four such variables by evaluating state-of-the-art visual localization methods as part of the motion planning module of an autonomous navigation stack. The results show major variation in the suitability of the different methods for vision-based navigation. To the authors' best knowledge, the proposed benchmark is the first to study modern visual localization methods as part of a complete navigation stack. We make the benchmark available at https://github.com/lasuomela/carla_vloc_benchmark.
AB - This work introduces a simulator-based benchmark for visual localization in the autonomous navigation context. The dynamic benchmark enables investigation of how variables such as the time of day, weather, and camera perspective affect the navigation performance of autonomous agents that utilize visual localization for closed-loop control. The experimental part of the paper studies the effects of four such variables by evaluating state-of-the-art visual localization methods as part of the motion planning module of an autonomous navigation stack. The results show major variation in the suitability of the different methods for vision-based navigation. To the authors' best knowledge, the proposed benchmark is the first to study modern visual localization methods as part of a complete navigation stack. We make the benchmark available at https://github.com/lasuomela/carla_vloc_benchmark.
KW - 3D computer vision
KW - Applications: Robotics
U2 - 10.1109/WACV56688.2023.00296
DO - 10.1109/WACV56688.2023.00296
M3 - Conference contribution
AN - SCOPUS:85149028132
T3 - Proceedings - 2023 IEEE Winter Conference on Applications of Computer Vision, WACV 2023
SP - 2944
EP - 2954
BT - Proceedings - 2023 IEEE Winter Conference on Applications of Computer Vision, WACV 2023
PB - IEEE
T2 - IEEE/CVF Winter Conference on Applications of Computer Vision
Y2 - 3 January 2023 through 7 January 2023
ER -