Multisensory Omni-directional Long-term Place Recognition: Benchmark Dataset and Analysis
This work addresses perceptual aliasing and appearance changes in place recognition for mobile robots and self-driving vehicles, but it is incremental as it builds on existing datasets and methods.
The authors tackled the problem of long-term place recognition for autonomous robots by introducing the MOLP dataset and analyzing feature extraction from omnidirectional images, concluding that intensity images outperform disparity images for discriminative features and enabling robust bidirectional loop closure detection.
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.