—In this paper, we present a novel end-to-end
learning-based LiDAR sensor relocalization framework,
termed PointLoc, which infers 6-DoF poses directly using only
a single point cloud as input. Compared to visual sensorbased relocalization, LiDAR sensors can provide rich and
robust geometric information about a scene. However, point
clouds of LiDAR sensors are unordered and unstructured
making it difficult to apply traditional deep learning regression
models for this task. We address this issue by proposing
a novel PointNet-style architecture with self-attention to efficiently estimate 6-DoF poses from 360◦ LiDAR sensor frames.
Extensive experiments on recently released challenging Oxford Radar RobotCar dataset and real-world robot experiments
demonstrate that the proposed method can achieve accurate relocalization performance.