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

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

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 …

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

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 …

Place recognition is crucial for robot localization and loop closure in simultaneous
localization and mapping (SLAM). Light Detection and Ranging (LiDAR), known for its …

Place recognition remains a challenging problem under various perceptual conditions, eg,
all weather, times of day, seasons, and viewpoint shifts. Different from most of the existing …

The recognition of places that have already been visited is a fundamental requirement for a
mobile robot. This particularly concerns the detection of loop closures while mapping …

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 …

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