CNN-Based Dense Monocular Visual SLAM for Real-Time UAV Exploration in Emergency Conditions

Steenbeek, Anne; Nex, Francesco

doi:10.3390/drones6030079

Cited by 50 publications

(30 citation statements)

References 46 publications

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“…It is also assumed that the robot can locate itself on the map and get the position of the charging pile. Map and localization of robot can be realized by using any SLAM (simultaneous localization and mapping) module that is already available in the literatures [23], [24], [25]. The navigation module is also considered available.…”

Section: A Localization and Communication Setupmentioning

confidence: 99%

Robot Memorial Path Planning for Smart Access of Indoor Distributed Charging Piles

Deng

Xie

et al. 2023

IEEE Access

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Autonomous mobile service robots are used to complete many tasks, such as cleaning, transporting goods and monitoring. Such tasks usually require uninterrupted and continuous service. However, the battery of the robot is limited and must be charged frequently. For a large number of robots, it is essential to select a suitable charging pile. For this issue, we propose a path planning model for robots to intelligently access a limited number of charging piles distributed on the map. The traditional path planning model mainly considers the shortest path criterion to generate the path. Different from this, the path planning model in this paper not only considers the shortest path, but also the service position of the robot after charging, the remaining power of the robot, the state of the charging pile and the position of the robot in the map. Our path planning assigns the most suitable charging pile to the robot that needs charging. To solve the problem of high memory consumption and slow search speed when traditional A* algorithm is used for path planning, we propose local memorial path planning (LMPP) algorithm to quickly generate effective paths. The simulation results show that the proposed robot charging path planner can improve the robot service satisfaction and plan the effective path to the available charging piles.INDEX TERMS Autonomous mobile robot, service robot, path planning, robot charging

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Section: A Localization and Communication Setupmentioning

confidence: 99%

Robot Memorial Path Planning for Smart Access of Indoor Distributed Charging Piles

Deng

Xie

et al. 2023

IEEE Access

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“…Image-based methods can provide rich cues of a scene, but some methods directly regressed control commands from a single image, and they did not utilize the geometric information of scenes [10]- [12], [25]. In comparison, methods using depth maps are favorable [13]- [15], [26] because controlling commands or path planning can be built upon this intermediate step, and the depth maps can be used for other tasks such as scene reconstruction. However, reliable depth estimation is computationally expensive, and the mentioned methods ran on larger platforms or off-board.…”

Section: A Obstacle Avoidance Of Nano Dronesmentioning

confidence: 99%

Channel-Aware Distillation Transformer for Depth Estimation on Nano Drones

Zhang¹,

Nex²,

Vosselman³

et al. 2023

Preprint

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Autonomous navigation of drones using computer vision has achieved promising performance. Nano-sized drones based on edge computing platforms are lightweight, flexible, and cheap, thus suitable for exploring narrow spaces. However, due to their extremely limited computing power and storage, vision algorithms designed for high-performance GPU platforms cannot be used for nano drones. To address this issue this paper presents a lightweight CNN depth estimation network deployed on nano drones for obstacle avoidance. Inspired by Knowledge Distillation (KD), a Channel-Aware Distillation Transformer (CADiT) is proposed to facilitate the small network to learn knowledge from a larger network. The proposed method is validated on the KITTI dataset and tested on a nano drone Crazyflie, with an ultra-low power microprocessor GAP8.

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“…CNN's advantages in image processing have been fully verified. For example, visual depth estimation improves the problem that monocular cameras cannot obtain reliable depth information [157]. In 2017, Tateno et al [158] proposed a real-time SLAM system "CNN-SLAM "based on CNN in the framework of LSD-SLAM.…”

Section: Cnn With Vslammentioning

confidence: 99%

An Overview on Visual SLAM: From Tradition to Semantic

et al. 2022

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Visual SLAM (VSLAM) has been developing rapidly due to its advantages of low-cost sensors, the easy fusion of other sensors, and richer environmental information. Traditional visionbased SLAM research has made many achievements, but it may fail to achieve wished results in challenging environments. Deep learning has promoted the development of computer vision, and the combination of deep learning and SLAM has attracted more and more attention. Semantic information, as high-level environmental information, can enable robots to better understand the surrounding environment. This paper introduces the development of VSLAM technology from two aspects: traditional VSLAM and semantic VSLAM combined with deep learning. For traditional VSLAM, we summarize the advantages and disadvantages of indirect and direct methods in detail and give some classical VSLAM open-source algorithms. In addition, we focus on the development of semantic VSLAM based on deep learning. Starting with typical neural networks CNN and RNN, we summarize the improvement of neural networks for the VSLAM system in detail. Later, we focus on the help of target detection and semantic segmentation for VSLAM semantic information introduction. We believe that the development of the future intelligent era cannot be without the help of semantic technology. Introducing deep learning into the VSLAM system to provide semantic information can help robots better perceive the surrounding environment and provide people with higher-level help.

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CNN-Based Dense Monocular Visual SLAM for Real-Time UAV Exploration in Emergency Conditions

Cited by 50 publications

References 46 publications

Robot Memorial Path Planning for Smart Access of Indoor Distributed Charging Piles

Robot Memorial Path Planning for Smart Access of Indoor Distributed Charging Piles

Channel-Aware Distillation Transformer for Depth Estimation on Nano Drones

An Overview on Visual SLAM: From Tradition to Semantic

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