LocNet: Global localization in 3D point clouds for mobile vehicles
This addresses the problem of estimating vehicle poses without prior knowledge for autonomous navigation, but appears incremental as it builds on existing methods for place recognition and pose estimation.
The paper tackled global localization in 3D point clouds for mobile vehicles by developing a semi-handcrafted representation learning method using siamese LocNets, achieving high accuracy in pose estimation on the KITTI dataset and multi-session datasets.
Global localization in 3D point clouds is a challenging problem of estimating the pose of vehicles without any prior knowledge. In this paper, a solution to this problem is presented by achieving place recognition and metric pose estimation in the global prior map. Specifically, we present a semi-handcrafted representation learning method for LiDAR point clouds using siamese LocNets, which states the place recognition problem to a similarity modeling problem. With the final learned representations by LocNet, a global localization framework with range-only observations is proposed. To demonstrate the performance and effectiveness of our global localization system, KITTI dataset is employed for comparison with other algorithms, and also on our long-time multi-session datasets for evaluation. The result shows that our system can achieve high accuracy.