Enhancing Autonomous Navigation: A Visual SLAM Approach

Abstract An autonomous vehicle can simultaneously map its environment and identify its own position by employing a technique called “Simultaneous Localisation And Mapping” (SLAM). Autonomous mobility requires identifying the locations of adjacent landmarks and objects, as well as the vehicle’s posit...

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Bibliographic Details
Published inJournal of physics. Conference series Vol. 2748; no. 1; pp. 12008 - 12015
Main Authors Paul, Sayandip, Hemanth Kumar, C, Arunkumar Bongale, C
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.04.2024
Subjects
Algorithms
Autonomous navigation
Cameras
Ego vehicle
Loop Closure
Monocular SLAM
Simultaneous localization and mapping
Space perception
Stereo SLAM
Trajectory analysis
Visual SLAM
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Summary:Abstract An autonomous vehicle can simultaneously map its environment and identify its own position by employing a technique called “Simultaneous Localisation And Mapping” (SLAM). Autonomous mobility requires identifying the locations of adjacent landmarks and objects, as well as the vehicle’s position, using an appropriate technique. Monocular SLAM systems often face challenges related to depth perception and scale ambiguity, leading to trajectory drift over time. In contrast, Stereo SLAM systems utilize dual cameras to overcome these limitations. The purpose of this work is to assess how well visual SLAM systems perform by contrasting trajectory estimates with ground truth information obtained from simulations. The findings indicate that stereo visual SLAM algorithms offer more accurate camera trajectory estimations than monocular SLAM, making them a preferable choice for applications demanding precise camera localization and mapping in autonomous vehicles.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2748/1/012008