Graph-based LiDAR-Inertial SLAM Enhanced by Loosely-Coupled Visual Odometry

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F23%3A00372387" target="_blank" >RIV/68407700:21230/23:00372387 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/ECMR59166.2023.10256360" target="_blank" >https://doi.org/10.1109/ECMR59166.2023.10256360</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ECMR59166.2023.10256360" target="_blank" >10.1109/ECMR59166.2023.10256360</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Graph-based LiDAR-Inertial SLAM Enhanced by Loosely-Coupled Visual Odometry

Popis výsledku v původním jazyce

In this paper, we address robot localization using Simultaneous Localization and Mapping (SLAM) with Light Detection and Ranging (LiDAR) perception enhanced by visual odometry in scenarios where laser scan matching can be ambiguous because of a lack of sufficient features in the scan. We propose a Graph-based SLAM approach that benefits from fusing data from multiple types of sensors to overcome the disadvantages of using only LiDAR data for localization. The proposed method uses a failure detection model based on the quality of the LiDAR scan matching and inertial measurement unit data. The failure model improves LiDAR-based localization by an additional localization source, including low-cost blackbox visual odometers like the Intel RealSense T265. The proposed method is compared to the state-of-the-art localization system LIO-SAM in cluttered and open urban areas. Based on the performed experimental deployments, the proposed failure detection model with black-box visual odometry sensor yields improved localization performance measured by the absolute trajectory and relative pose error indicators.

Název v anglickém jazyce

Graph-based LiDAR-Inertial SLAM Enhanced by Loosely-Coupled Visual Odometry

Popis výsledku anglicky

In this paper, we address robot localization using Simultaneous Localization and Mapping (SLAM) with Light Detection and Ranging (LiDAR) perception enhanced by visual odometry in scenarios where laser scan matching can be ambiguous because of a lack of sufficient features in the scan. We propose a Graph-based SLAM approach that benefits from fusing data from multiple types of sensors to overcome the disadvantages of using only LiDAR data for localization. The proposed method uses a failure detection model based on the quality of the LiDAR scan matching and inertial measurement unit data. The failure model improves LiDAR-based localization by an additional localization source, including low-cost blackbox visual odometers like the Intel RealSense T265. The proposed method is compared to the state-of-the-art localization system LIO-SAM in cluttered and open urban areas. Based on the performed experimental deployments, the proposed failure detection model with black-box visual odometry sensor yields improved localization performance measured by the absolute trajectory and relative pose error indicators.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA22-05762S" target="_blank" >GA22-05762S: Optimální řešení robotických směrovacích úloh</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of 11th European Conference on Mobile Robots

ISBN

979-8-3503-0704-7

ISSN

2639-7919

e-ISSN

—

Počet stran výsledku

8

Strana od-do

278-285

Název nakladatele

Institute of Electrical and Electronics Engineers

Místo vydání

Brighton

Místo konání akce

Coimbra

Datum konání akce

4. 9. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001082260500041