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Bibliographic Details
Main Authors: Relia, Lomash, Singla, Jai G, Amitabh, Dube, Nitant
Format: Preprint
Published: 2026
Subjects:
Online Access:https://arxiv.org/abs/2604.22331
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author Relia, Lomash
Singla, Jai G
Amitabh
Dube, Nitant
author_facet Relia, Lomash
Singla, Jai G
Amitabh
Dube, Nitant
contents This study analyses simulated and real-world implementations of depth-aware rover navigation, highlighting the transition from stereo vision to monocular depth estimation using edge AI. A Unity-based lunar terrain simulator with stereo cameras and OpenCV's StereoSGBM was used to generate disparity maps. A physical rover built on Raspberry Pi 4 employed UniDepthV2 for monocular metric depth estimation and YOLO12n for real-time object detection. While stereo vision yielded higher accuracy in simulation, the monocular approach proved more robust and cost-effective in real-world deployment, achieving 0.1 FPS for depth and 10 FPS for detection.
format Preprint
id arxiv_https___arxiv_org_abs_2604_22331
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Depth-Aware Rover: A Study of Edge AI and Monocular Vision for Real-World Implementation
Relia, Lomash
Singla, Jai G
Amitabh
Dube, Nitant
Computer Vision and Pattern Recognition
This study analyses simulated and real-world implementations of depth-aware rover navigation, highlighting the transition from stereo vision to monocular depth estimation using edge AI. A Unity-based lunar terrain simulator with stereo cameras and OpenCV's StereoSGBM was used to generate disparity maps. A physical rover built on Raspberry Pi 4 employed UniDepthV2 for monocular metric depth estimation and YOLO12n for real-time object detection. While stereo vision yielded higher accuracy in simulation, the monocular approach proved more robust and cost-effective in real-world deployment, achieving 0.1 FPS for depth and 10 FPS for detection.
title Depth-Aware Rover: A Study of Edge AI and Monocular Vision for Real-World Implementation
topic Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2604.22331