Summary of Overlapmamba: Novel Shift State Space Model For Lidar-based Place Recognition, by Qiuchi Xiang et al.
OverlapMamba: Novel Shift State Space Model for LiDAR-based Place Recognition
by Qiuchi Xiang, Jintao Cheng, Jiehao Luo, Jin Wu, Rui Fan, Xieyuanli Chen, Xiaoyu Tang
First submitted to arxiv on: 13 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel deep learning model called OverlapMamba for place recognition in autonomous systems. This model represents input range views as sequences and uses state space models to compress visual representations. The method is evaluated on three public datasets, showing robustness in detecting loop closures even when traversing previously visited locations from different directions. Compared to traditional LiDAR and multi-view combination methods, OverlapMamba outperforms them in time complexity and speed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a new way for autonomous systems to recognize places they’ve been before. It uses a special kind of computer model called OverlapMamba that takes in images from sensors like lidar. This model is good at finding patterns in these images, which helps it figure out where it is and what’s around it. The paper shows that this method works well on different public datasets and is faster than other methods that use multiple types of data. |
Keywords
» Artificial intelligence » Deep learning
