J-MOD<sup>2</sup>: Joint Monocular Obstacle Detection and Depth Estimation

Mancini, Michele; Valigi, Paolo; Ciarfuglia, Thomas A.

doi:10.1109/lra.2018.2800083

Cited by 58 publications

(31 citation statements)

References 33 publications

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“…The development of deep learning has revolutionized machine vision, and there are lots of research results in this field. For example, Mancini, et al [46] proposed a CNN architecture, which can jointly finish the learning task for depth estimation and obstacle detection. Chen [47] presented a monocular vision-based algorithm.…”

Section: Obstacle Detectionmentioning

confidence: 99%

A Survey on Theories and Applications for Self-Driving Cars Based on Deep Learning Methods

Chen

et al. 2020

Applied Sciences

118

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Self-driving cars are a hot research topic in science and technology, which has a great influence on social and economic development. Deep learning is one of the current key areas in the field of artificial intelligence research. It has been widely applied in image processing, natural language understanding, and so on. In recent years, more and more deep learning-based solutions have been presented in the field of self-driving cars and have achieved outstanding results. This paper presents a review of recent research on theories and applications of deep learning for self-driving cars. This survey provides a detailed explanation of the developments of self-driving cars and summarizes the applications of deep learning methods in the field of self-driving cars. Then the main problems in self-driving cars and their solutions based on deep learning methods are analyzed, such as obstacle detection, scene recognition, lane detection, navigation and path planning. In addition, the details of some representative approaches for self-driving cars using deep learning methods are summarized. Finally, the future challenges in the applications of deep learning for self-driving cars are given out.

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Section: Obstacle Detectionmentioning

confidence: 99%

A Survey on Theories and Applications for Self-Driving Cars Based on Deep Learning Methods

Chen

et al. 2020

Applied Sciences

118

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“…A multi-task deep learning model, which estimates the depth of a scene and extracts the obstacles without the need to compute a global map with an application in micro air vehicle flights, has been proposed in [35]. Other, mainly preliminary, studies have approached the obstacle detection problem for the safe navigation of VCP as a 3D problem by using images along with depth information and enhancing the performance by exploiting the capabilities of CNN models [36][37][38].…”

Section: Obstacle Detectionmentioning

confidence: 99%

Uncertainty-Aware Visual Perception System for Outdoor Navigation of the Visually Challenged

Dimas

Diamantis

Kalozoumis

et al. 2020

Sensors

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Every day, visually challenged people (VCP) face mobility restrictions and accessibility limitations. A short walk to a nearby destination, which for other individuals is taken for granted, becomes a challenge. To tackle this problem, we propose a novel visual perception system for outdoor navigation that can be evolved into an everyday visual aid for VCP. The proposed methodology is integrated in a wearable visual perception system (VPS). The proposed approach efficiently incorporates deep learning, object recognition models, along with an obstacle detection methodology based on human eye fixation prediction using Generative Adversarial Networks. An uncertainty-aware modeling of the obstacle risk assessment and spatial localization has been employed, following a fuzzy logic approach, for robust obstacle detection. The above combination can translate the position and the type of detected obstacles into descriptive linguistic expressions, allowing the users to easily understand their location in the environment and avoid them. The performance and capabilities of the proposed method are investigated in the context of safe navigation of VCP in outdoor environments of cultural interest through obstacle recognition and detection. Additionally, a comparison between the proposed system and relevant state-of-the-art systems for the safe navigation of VCP, focused on design and user-requirements satisfaction, is performed.

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“…[19] proposes a behavior arbitration scheme to obtain the yaw and pitch angles for the UAV to avoid an obstacle and for navigation in general. Trajectory planning using obstacle bounding boxes and depth estimation is explored in [20]. This work designs a CNN architecture that jointly estimates depth and obstacle bounding boxes.…”

Section: Introductionmentioning

confidence: 99%

Memory-Based Deep Reinforcement Learning for Obstacle Avoidance in UAV With Limited Environment Knowledge

Singla

Padakandla

Bhatnagar

2021

IEEE Trans. Intell. Transport. Syst.

183

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This paper presents our method for enabling a UAV quadrotor, equipped with a monocular camera, to autonomously avoid collisions with obstacles in unstructured and unknown indoor environments. When compared to obstacle avoidance in ground vehicular robots, UAV navigation brings in additional challenges because the UAV motion is no more constrained to a well-defined indoor ground or street environment. Horizontal structures in indoor and outdoor environments like decorative items, furnishings, ceiling fans, sign-boards, tree branches etc., also become relevant obstacles unlike those for ground vehicular robots. Thus, methods of obstacle avoidance developed for ground robots are clearly inadequate for UAV navigation. Current control methods using monocular images for UAV obstacle avoidance are heavily dependent on environment information. These controllers do not fully retain and utilize the extensively available information about the ambient environment for decision making. We propose a deep reinforcement learning based method for UAV obstacle avoidance (OA) and autonomous exploration which is capable of doing exactly the same. The crucial idea in our method is the concept of partial observability and how UAVs can retain relevant information about the environment structure to make better future navigation decisions. Our OA technique uses recurrent neural networks with temporal attention and provides better results compared to prior works in terms of distance covered during navigation without collisions. In addition, our technique has a high inference rate (a key factor in robotic applications) and is energy-efficient as it minimizes oscillatory motion of UAV and reduces power wastage.

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J-MOD²: Joint Monocular Obstacle Detection and Depth Estimation

Cited by 58 publications

References 33 publications

A Survey on Theories and Applications for Self-Driving Cars Based on Deep Learning Methods

A Survey on Theories and Applications for Self-Driving Cars Based on Deep Learning Methods

Uncertainty-Aware Visual Perception System for Outdoor Navigation of the Visually Challenged

Memory-Based Deep Reinforcement Learning for Obstacle Avoidance in UAV With Limited Environment Knowledge

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J-MOD2: Joint Monocular Obstacle Detection and Depth Estimation

Cited by 58 publications

References 33 publications

A Survey on Theories and Applications for Self-Driving Cars Based on Deep Learning Methods

A Survey on Theories and Applications for Self-Driving Cars Based on Deep Learning Methods

Uncertainty-Aware Visual Perception System for Outdoor Navigation of the Visually Challenged

Memory-Based Deep Reinforcement Learning for Obstacle Avoidance in UAV With Limited Environment Knowledge

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Product

Resources

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J-MOD²: Joint Monocular Obstacle Detection and Depth Estimation