LiDAR-based place recognition (LPR) plays a pivotal role in autonomous driving, which
assists Simultaneous Localization and Mapping (SLAM) systems in reducing accumulated …

Deep learning (DL) has become one of the mainstream and effective methods for point
cloud analysis tasks such as detection, segmentation and classification. To reduce …

Place recognition is an important capability for autonomously navigating vehicles operating
in complex environments and under changing conditions. It is a key component for tasks …

Recently, deep learning based point cloud descriptors have achieved impressive results in
the place recognition task. Nonetheless, due to the sparsity of point clouds, how to extract …

We tackle the problem of 3D point cloud localization based on a few natural linguistic
descriptions and introduce a novel neural network Text2Loc that fully interprets the semantic …

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 …

Place recognition is an important component for autonomous vehicles to achieve loop
closing or global localization. In this article, we tackle the problem of place recognition …

Abstract Knowledge about the own pose is key for all mobile robot applications. Thus pose
estimation is part of the core functionalities of mobile robots. Over the last two decades …

Place recognition is an important branch of Simultaneous Localization and Mapping
(SLAM), which finds revisited places, thereby reducing error accumulations. Most …

LiDAR-based place recognition (LPR) is one of the most crucial components of autonomous
vehicles to identify previously visited places in GPS-denied environments. Most existing LPR …