LiDAR has gained popularity in autonomous driving due to advantages like long
measurement distance, rich 3D information, and stability in harsh environments. Place …

Loop closure detection is of vital importance in the process of simultaneous localization and
mapping (SLAM), as it helps to reduce the cumulative error of the robot's estimated pose and …

Place recognition is a key module in robotic navigation. The existing line of studies mostly
focuses on visual place recognition to recognize previously visited places solely based on …

The rapid development of autonomous driving and mobile mapping calls for off-the-shelf
LiDAR SLAM solutions that are adaptive to LiDARs of different specifications on various …

This paper introduces a multimodal range dataset namely for radio detection and ranging
(radar) and light detection and ranging (LiDAR) specifically targeting the urban environment …

Place recognition in 3D data is a challenging task that has been commonly approached by
adapting image-based solutions. Methods based on local features suffer from ambiguity and …

We present a novel static point cloud map construction algorithm, called Removert, for use
within dynamic urban environments. Leaving only static points and excluding dynamic …

Due to the difficulty in generating the effective descriptors which are robust to occlusion and
viewpoint changes, place recognition for 3D point cloud remains an open issue. Unlike most …

Place recognition gives a SLAM system the ability to correct cumulative errors. Unlike
images that contain rich texture features, point clouds are almost pure geometric information …

LiDAR-based SLAM system is admittedly more accurate and stable than others, while its
loop closure detection is still an open issue. With the development of 3D semantic …