Compared to diverse feature detectors and descriptors used for visual scenes, describing a
place using structural information is relatively less reported. Recent advances in …

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

Retrieval-based place recognition is an efficient and effective solution for re-localization
within a pre-built map, or global data association for Simultaneous Localization and …

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 …

3D point cloud-based place recognition is highly demanded by autonomous driving in GPS-
challenged environments and serves as an essential component (ie loop-closure detection) …

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 …

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 …

In this work, we present a novel global descriptor termed stable triangle descriptor (STD) for
3D place recognition. For a triangle, its shape is uniquely determined by the length of the …

For autonomous driving, it is important to navigate in an unknown environment. In this paper,
we propose a fully automated 2D simultaneous localization and mapping (SLAM) system …