A Region-Based Approach to Monocular Mapless Navigation Using Deep Learning Techniques

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Abstract

In recent years, there have been significant advances in navigation methods for autonomous robotic systems, giving rise to a diverse range of navigation techniques. These techniques include GPS-based, SLAM-based, and monocular depth-based navigation. However, each of these approaches has its limitations. Typically, these techniques rely on either external sensors and positioning systems or require the creation of a local map prior to initiating navigation. This paper introduces a new approach for autonomous navigation of ground robots: mapless navigation using a pre-trained monocular depth network. This technique offers an efficient and cost-effective way of navigating without the need for a pre-existing map of the environment. To evaluate and compare the performance of our method, we conducted experiments using two different depth estimation models tested within the Gazebo simulation environment.

OriginalsprogEngelsk
Titel9th 2023 International Conference on Control, Decision and Information Technologies, CoDIT 2023
Antal sider6
ForlagIEEE (Institute of Electrical and Electronics Engineers)
Publikationsdatookt. 2023
Sider1635 - 1640
ISBN (Trykt)979-8-3503-1141-9
ISBN (Elektronisk)979-8-3503-1140-2
DOI
StatusUdgivet - okt. 2023
Begivenhed9th International Conference on Control, Decision and Information Technologies (CoDIT) - Rome, Italien
Varighed: 3 jul. 20236 jul. 2023
https://codit2023.com/

Konference

Konference9th International Conference on Control, Decision and Information Technologies (CoDIT)
Land/OmrådeItalien
ByRome
Periode03/07/202306/07/2023
Internetadresse
NavnInternational Conference on Control, Decision and Information Technologies (CoDIT)
ISSN2576-3555

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