Multisensory Omni-directional Long-term Place Recognition: Benchmark Dataset and Analysis
Ashwin Mathur, Fei Han, and Hao Zhang
TL;DR
This paper introduces MOLP, a comprehensive dataset with omnidirectional images for long-term place recognition, and proposes a convex optimization approach showing intensity images outperform disparity images in feature discrimination.
Contribution
The paper presents a new multisensory omnidirectional dataset for long-term place recognition and formulates a novel convex optimization method leveraging intensity images for improved robustness.
Findings
Intensity images outperform disparity images in feature discrimination.
Omnidirectional sensors enable robust bidirectional loop closure detection.
The proposed method effectively handles long-term appearance changes.
Abstract
Recognizing a previously visited place, also known as place recognition (or loop closure detection) is the key towards fully autonomous mobile robots and self-driving vehicle navigation. Augmented with various Simultaneous Localization and Mapping techniques (SLAM), loop closure detection allows for incremental pose correction and can bolster efficient and accurate map creation. However, repeated and similar scenes (perceptual aliasing) and long term appearance changes (e.g. weather variations) are major challenges for current place recognition algorithms. We introduce a new dataset Multisensory Omnidirectional Long-term Place recognition (MOLP) comprising omnidirectional intensity and disparity images. This dataset presents many of the challenges faced by outdoor mobile robots and current place recognition algorithms. Using MOLP dataset, we formulate the place recognition problem as a…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsRobotics and Sensor-Based Localization · Indoor and Outdoor Localization Technologies · Advanced Image and Video Retrieval Techniques
11institutetext: 11email: [email protected], [email protected], [email protected] 33institutetext: Colorado School Of Mines, Golden, CO 80401, USA.
Multisensory Omni-directional Long-term Place Recognition: Benchmark Dataset and Analysis
Ashwin Mathur
Fei Han
and Hao Zhang
Abstract
Recognizing a previously visited place, also known as place recognition (or loop closure detection) is the key towards fully autonomous mobile robots and self-driving vehicle navigation. Augmented with various Simultaneous Localization and Mapping techniques (SLAM), loop closure detection allows for incremental pose correction and can bolster efficient and accurate map creation. However, repeated and similar scenes (perceptual aliasing) and long term appearance changes (e.g. weather variations) are major challenges for current place recognition algorithms. We introduce a new dataset Multisensory Omnidirectional Long-term Place recognition (MOLP) comprising omnidirectional intensity and disparity images. This dataset presents many of the challenges faced by outdoor mobile robots and current place recognition algorithms. Using MOLP dataset, we formulate the place recognition problem as a regularized sparse convex optimization problem. We conclude that information extracted from intensity image is superior to disparity image in isolating discriminative features for successful long term place recognition. Furthermore, when these discriminative features are extracted from an omnidirectional vision sensor, a robust bidirectional loop closure detection approach is established, allowing mobile robots to close the loop, regardless of the difference in the direction when revisiting a place.