Simultaneous localization and mapping of medical burn areas based on binocular vision and capsule networks

Wu, Xiaolin; He-ming, Chen; Wu, Xiaoli; Wu, Shunjun; Huang, Jing

doi:10.1007/s00500-020-05067-4

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…Here, the visual SLAM approach can be used to understand the environment and navigate [7][8][9]. Segmentation can identify the traversable plane that the robots' advances from the visual image or point cloud [10][11][12][13]. Laser, sonar and other ranged sensors can also detect obstacles and their shape and position [14,15].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Technique for Active SLAM Based on RPPO Model with Transfer Learning

Wen

Rad

et al. 2022

Preprint

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The problem of exploration in unknown environments is still a great challenge for autonomous mobile robots due to the lack of a priori knowledge. Active Simultaneous Localization and Mapping (SLAM) is an effective method to realize obstacle avoidance and autonomous navigation. Traditional Active SLAM is usually complex to model and difficult to adapt automatically to new operating areas. This paper presents a novel Active SLAM algorithm based on Deep Reinforcement Learning (DRL). The Relational Proximal Policy Optimization (RPPO) model with deep separable convolution and data batch processing is used to predict the action strategy and generate the action plan through the acquired environment RGB images, so as to realize the autonomous collision free exploration of the environment. Meanwhile, Gmapping is applied to locate and map the environment. Then, based on Transfer Learning, Active SLAM algorithm is applied to complex unknown environments with various dynamic and static obstacles. Finally, we present several experiments to demonstrate the advantages and feasibility of the proposed Active SLAM algorithm.

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Section: Introductionmentioning

confidence: 99%

A Hybrid Technique for Active SLAM Based on RPPO Model with Transfer Learning

Wen

Rad

et al. 2022

Preprint

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“…Indeed, systems based on vision for measurement can be automated so that they provide accurate measurements and systematic errors can be handled efficiently [6]. With the advancement of technology, stereo vision systems have been widely used in many fields of study such as obstacle detection [7,8], 3D reconstruction [9,10], medical imaging [11,12], film industry, outdoor applications [13,14], and object recognition since it can perform specific measurement tasks on its own. The stereo vision system does not require complicated additional equipment; it is able to identify the structure and motion parameters of the target in motion using the information provided by the two cameras.…”

mentioning

confidence: 99%

A novel algorithm for distance measurement using stereo camera

Adil

Mikou

Mouhsen

2022

CAAI Trans on Intel Tech

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Distance estimation can be achieved by using active sensors or with the help of passive sensors such as cameras. The stereo vision system is generally composed of two cameras to mimic the human binocular vision. In this paper, a Python‐based algorithm is proposed to find the parameters of each camera, rectify the images, create the disparity maps and finally use these maps for distance measurements. Experiments using real‐time images, which were captured from our stereo vision system, of different obstacles positioned at multiple distances (60–200 cm) prove the effectiveness of the proposed program and show that the calculated distance to the obstacle is relatively accurate. The accuracy of distance measurement is up to 99.83%. The processing time needed to calculate the distance between the obstacle and the cameras is less than 0.355 s.

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