Place recognition is one of the most crucial modules for autonomous vehicles
to identify places that were previously visited in GPS-invalid environments.
Sensor fusion is considered an effective method to overcome the weaknesses of
individual sensors. In recent years, multimodal place recognition fusing
information from multiple sensors has gathered increasing attention. However,
most existing multimodal place recognition methods only use limited
field-of-view camera images, which leads to an imbalance between features from
different modalities and limits the effectiveness of sensor fusion. In this
paper, we present a novel neural network named LCPR for robust multimodal place
recognition, which fuses LiDAR point clouds with multi-view RGB images to
generate discriminative and yaw-rotation invariant representations of the
environment. A multi-scale attention-based fusion module is proposed to fully
exploit the panoramic views from different modalities of the environment and
their correlations. We evaluate our method on the nuScenes dataset, and the
experimental results show that our method can effectively utilize multi-view
camera and LiDAR data to improve the place recognition performance while
maintaining strong robustness to viewpoint changes. Our open-source code and
pre-trained models are available at this https URL .

提出了一种名为 LCPR 的新型神经网络，该网络融合 LiDAR 点云和多视角 RGB 图像，生成环境的具有区分性和偏航旋转不变性的表示，从而提高了地点识别性能并保持了对视角变化的强鲁棒性。