I2P-Rec: Recognizing Images on Large-scale Point Cloud Maps through Bird's Eye View Projections
This addresses a cross-modal matching challenge in autonomous driving, enabling image-based localization on point cloud maps, but it is incremental as it builds on existing depth estimation and BEV projection techniques.
The paper tackles the problem of localizing images on large-scale point cloud maps for autonomous cars by proposing the I2P-Rec method, which transforms cross-modal data into Bird's Eye View images for matching, achieving recall rates over 80% for monocular and 90% for stereo images on the KITTI dataset.
Place recognition is an important technique for autonomous cars to achieve full autonomy since it can provide an initial guess to online localization algorithms. Although current methods based on images or point clouds have achieved satisfactory performance, localizing the images on a large-scale point cloud map remains a fairly unexplored problem. This cross-modal matching task is challenging due to the difficulty in extracting consistent descriptors from images and point clouds. In this paper, we propose the I2P-Rec method to solve the problem by transforming the cross-modal data into the same modality. Specifically, we leverage on the recent success of depth estimation networks to recover point clouds from images. We then project the point clouds into Bird's Eye View (BEV) images. Using the BEV image as an intermediate representation, we extract global features with a Convolutional Neural Network followed by a NetVLAD layer to perform matching. The experimental results evaluated on the KITTI dataset show that, with only a small set of training data, I2P-Rec achieves recall rates at Top-1\% over 80\% and 90\%, when localizing monocular and stereo images on point cloud maps, respectively. We further evaluate I2P-Rec on a 1 km trajectory dataset collected by an autonomous logistics car and show that I2P-Rec can generalize well to previously unseen environments.