@inproceedings{
zhang2024deeppointmap,
title={DeepPointMap2: Accurate and Robust Li{DAR}-Visual {SLAM} with Neural Descriptors},
author={Xiaze Zhang and Ziheng Ding and Qi Jing and Ying Cheng and Wenchao Ding and Rui Feng},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=05PCDMN4Dk}
}
Simultaneous Localization and Mapping (SLAM) plays a pivotal role in autonomous driving and robotics. Given the complexity of road environments, there is a growing research emphasis on developing robust and accurate multi-modal SLAM systems. Existing methods often rely on hand-craft feature extraction and cross-modal fusion techniques, resulting in limited feature representation capability and reduced flexibility and robustness. To address this challenge, we introduce DeepPointMap2, a novel learning-based LiDAR-Visual SLAM architecture that leverages neural descriptors to tackle multiple SLAM subtasks in a unified manner. Our approach employs neural networks to extract multi-modal feature tokens, which are then adaptively fused by the Visual-Point Fusion Module to generate sparse neural 3D descriptors, ensuring precise localization and robust performance. As a pioneering work, our method achieves state-of-the-art localization performance among various Visual-based, LiDAR-based, and Visual-LiDAR-based methods in widely used benchmarks, as shown in the experiment results. Furthermore, the approach proves to be robust in scenarios involving camera failure and LiDAR obstruction.
DeepPointMap2: Accurate and Robust LiDAR-Visual SLAM with Neural Descriptors
Xiaze Zhang Ziheng Ding Qi Jing Ying Cheng Wenchao Ding Rui Feng