PADLoC

A key component of graph-based SLAM systems is the ability to detect loop closures in a trajectory to reduce the drift accumulated over time from the odometry. Most LiDAR-based methods achieve this goal by using only the geometric information, disregarding the semantics of the scene. In this work, we introduce PADLoC, a LiDAR-based loop closure detection and registration architecture comprising a shared 3D convolutional feature extraction backbone, a global descriptor head for loop closure detection, and a novel transformer-based head for point cloud matching and registration. We present multiple methods for estimating the point-wise matching confidence based on diversity indices. Additionally, to improve forward-backward consistency, we propose the use of two shared matching and registration heads with their source and target inputs swapped by exploiting that the estimated relative transformations must be inverse of each other. Furthermore, we leverage panoptic information during training in the form of a novel loss function that reframes the matching problem as a classification task in the case of the semantic labels and as a graph connectivity assignment for the instance labels. We perform extensive evaluations of PADLoC on multiple real-world datasets demonstrating that it achieves state-of-the-art performance.

We propose to leverage panoptic information to register two point clouds. In detail, we formulate the following losses. In the semantic loss, we treat the matching process as a classification task, where semantic class labels are predicted for the projected positive keypoints. We then take the mean absolute error of these predicted labels with respect of those of the corresponding anchor points. In case of data mislabeling, and to allow some tolerance in the semantic classification loss, we group the semantic labels into super-classes such as "vehicles" or "structures". We then evaluate the mean absolute error of the predicted meta-class labels into a meta-semantic loss function that is more heavily penalized. Finally, not only do we penalize matchings between points belonging to different semantic classes. In addition, in our novel multi-matched object loss, we further constrain the correspondences so that a subset of points belonging to a single object in the anchor sample are matched to a points from a single object in the positive sample. To avoid double-counting, this loss does not consider the semantic classes.