DeepAI AI Chat
Log In Sign Up

OREOS: Oriented Recognition of 3D Point Clouds in Outdoor Scenarios

by   Lukas Schaupp, et al.

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 LiDAR scans. These can be used both to retrieve near-by place candidates from a map, and to estimate the yaw discrepancy needed for bootstrapping local registration methods. We employ a triplet loss function for training and use a hard-negative mining strategy to further increase the performance of our descriptor extractor. In an evaluation on the NCLT and KITTI datasets, we demonstrate that our method outperforms related state-of-the-art approaches based on both data-driven and handcrafted data representation in challenging long-term outdoor conditions.


Place Recognition for Stereo VisualOdometry using LiDAR descriptors

Place recognition is a core component in SLAM, and in most visual SLAM s...

Locus: LiDAR-based Place Recognition using Spatiotemporal Higher-Order Pooling

Place Recognition (PR) enables the estimation of a globally consistent m...

Object Scan Context: Object-centric Spatial Descriptor for Place Recognition within 3D Point Cloud Map

Place recognition technology endows a SLAM algorithm with the ability to...

RPSRNet: End-to-End Trainable Rigid Point Set Registration Network using Barnes-Hut 2^D-Tree Representation

We propose RPSRNet - a novel end-to-end trainable deep neural network fo...

MinkLoc3D-SI: 3D LiDAR place recognition with sparse convolutions, spherical coordinates, and intensity

The 3D LiDAR place recognition aims to estimate a coarse localization in...

Spherical Multi-Modal Place Recognition for Heterogeneous Sensor Systems

In this paper, we propose a robust end-to-end multi-modal pipeline for p...

Real-time Registration and Reconstruction with Cylindrical LiDAR Images

Spinning LiDAR data are prevalent for 3D perception tasks, yet its cylin...