Many existing datasets for lidar place recognition are solely representative of structured
urban environments, and have recently been saturated in performance by deep learning …

Abstract Point Cloud Place Recognition (PCPR) in street scenes is an essential task in the
fields of autonomous driving, robot navigation, and urban map updating. However, the …

Loop closing and relocalization are crucial techniques to establish reliable and robust long-
term SLAM by addressing pose estimation drift and degeneration. This article begins by …

The success of re-localisation has crucial implications for the practical deployment of robots
operating within a prior map or relative to one another in real-world scenarios. Using single …

One-shot LiDAR localization refers to the ability to estimate the robot pose from one single
point cloud, which yields significant advantages in initialization and relocalization …

Large-scale LiDAR mappings and localization leverage place recognition techniques to
mitigate odometry drifts, ensuring accurate mapping. These techniques utilize scene …

In this letter, we propose a heterogeneous deep metric learning pipeline for ground and
aerial point cloud-based place recognition in large-scale environments. The pipeline …

LiDAR place recognition approaches based on deep learning suffer from significant
performance degradation when there is a shift between the distribution of training and test …

LiDAR-based place recognition (LPR) aims to localize autonomous vehicles and mobile
robots relative to pre-built maps or retrieve previously visited places. However, the …

Autonomous driving systems often require reliable loop closure detection to guarantee
reduced localization drift. Recently, 3D LiDAR-based localization methods have used …