TY - JOUR
T1 - Monocular Based Navigation System for Autonomous Ground Robots Using Multiple Deep Learning Models
AU - Machkour, Zakariae
AU - Ortiz Arroyo, Daniel
AU - Durdevic, Petar
PY - 2023/5/10
Y1 - 2023/5/10
N2 - In recent years, the development of ground robots with human-like perception capabilities has led to the use of multiple sensors, including cameras, lidars, and radars, along with deep learning techniques for detecting and recognizing objects and estimating distances. This paper proposes a computer vision-based navigation system that integrates object detection, segmentation, and monocular depth estimation using deep neural networks to identify predefined target objects and navigate towards them with a single monocular camera as a sensor. Our experiments include different sensitivity analyses to evaluate the impact of monocular cues on distance estimation. We show that this system can provide a ground robot with the perception capabilities needed for autonomous navigation in unknown indoor environments without the need for prior mapping or external positioning systems. This technique provides an efficient and cost-effective means of navigation, overcoming the limitations of other navigation techniques such as GPS-based and SLAM-based navigation.
AB - In recent years, the development of ground robots with human-like perception capabilities has led to the use of multiple sensors, including cameras, lidars, and radars, along with deep learning techniques for detecting and recognizing objects and estimating distances. This paper proposes a computer vision-based navigation system that integrates object detection, segmentation, and monocular depth estimation using deep neural networks to identify predefined target objects and navigate towards them with a single monocular camera as a sensor. Our experiments include different sensitivity analyses to evaluate the impact of monocular cues on distance estimation. We show that this system can provide a ground robot with the perception capabilities needed for autonomous navigation in unknown indoor environments without the need for prior mapping or external positioning systems. This technique provides an efficient and cost-effective means of navigation, overcoming the limitations of other navigation techniques such as GPS-based and SLAM-based navigation.
KW - Image segmentation
KW - Mapless navigation
KW - Monocular depth
KW - Object detection
KW - Obstacle avoidance
KW - Sensitivity analysis
UR - http://www.scopus.com/inward/record.url?scp=85159064381&partnerID=8YFLogxK
U2 - 10.1007/s44196-023-00250-5
DO - 10.1007/s44196-023-00250-5
M3 - Journal article
SN - 1875-6891
VL - 16
SP - 79
EP - 97
JO - International Journal of Computational Intelligence Systems
JF - International Journal of Computational Intelligence Systems
M1 - 79
ER -