LiDAR-based place recognition (LPR) is one of the basic capabilities of robots, which can
retrieve scenes from maps and identify previously visited locations based on 3D point …

We introduce a novel method for oriented place recognition with 3D LiDAR scans. A
Convolutional Neural Network is trained to extract compact descriptors from single 3D …

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 …

The 3D LiDAR place recognition aims to estimate a coarse localization in a previously seen
environment based on a single scan from a rotating 3D LiDAR sensor. The existing solutions …

Accurate and robust place recognition is essential for robot navigation, yet achieving full
pose invariance and high performance across diverse scenes remains challenging. In this …

Place Recognition enables the estimation of a globally consistent map and trajectory by
providing non-local constraints in Simultaneous Localisation and Mapping (SLAM). This …

Place recognition retrieves scenes from a prior map and identifies previously visited
locations. It is essential to autonomous-driving and robotics in terms of long-term navigation …

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 …

Recognizing places using Lidar in large-scale environments is challenging due to the
sparse nature of point cloud data. In this letter we present BVMatch, a Lidar-based frame-to …

We propose a methodology for robust, real-time place recognition using an imaging lidar,
which yields image-quality high-resolution 3D point clouds. Utilizing the intensity readings of …