LiDAR has gained popularity in autonomous driving due to advantages like long
measurement distance, rich three-dimensional information, and stability in harsh …

Although convolutional neural networks (CNNs) have achieved great success in computer
vision, this work investigates a simpler, convolution-free backbone network useful for many …

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 …

Given the prominence of 3D sensors in recent years, 3D point cloud scene data are worthy
to be further investigated. Point cloud scene understanding is a challenging task because of …

Place recognition based on point clouds (LiDAR) is an important component for autonomous
robots or self-driving vehicles. Current SOTA performance is achieved on accumulated …

Place recognition is a key module for long-term SLAM systems. Current LiDAR-based place
recognition methods usually use representations of point clouds such as unordered points …

The paper presents a simple and effective learning-based method for computing a
discriminative 3D point cloud descriptor for place recognition purposes. Recent state-of-the …

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 …

We introduce a discriminative multimodal descriptor based on a pair of sensor readings: a
point cloud from a LiDAR and an image from an RGB camera. Our descriptor, named …

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 …