scholarly journals Localization and Navigation for Autonomous Mobile Robots Using Petri Nets in Indoor Environments

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 31665-31676 ◽  
Author(s):  
Francisco A. X. Da Mota ◽  
Matheus Xavier Rocha ◽  
Joel J. P. C. Rodrigues ◽  
Victor Hugo C. De Albuquerque ◽  
Auzuir Ripardo De Alexandria
Keyword(s):  
Mobile Robots ◽  
Petri Nets ◽  
Autonomous Mobile Robots ◽  
Indoor Environments

scholarly journals SGC-VSLAM: A Semantic and Geometric Constraints VSLAM for Dynamic Indoor Environments

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2432
Author(s):  
Shiqiang Yang ◽  
Guohao Fan ◽  
Lele Bai ◽  
Cheng Zhao ◽  
Dexin Li
Keyword(s):  
Mobile Robots ◽  
Point Cloud ◽  
Feature Detection ◽  
Geometric Constraints ◽  
Autonomous Mobile Robots ◽  
Selection Strategy ◽  
Dynamic Feature ◽  
Indoor Environments ◽  
Dynamic Features ◽  
Static Point

As one of the core technologies for autonomous mobile robots, Visual Simultaneous Localization and Mapping (VSLAM) has been widely researched in recent years. However, most state-of-the-art VSLAM adopts a strong scene rigidity assumption for analytical convenience, which limits the utility of these algorithms for real-world environments with independent dynamic objects. Hence, this paper presents a semantic and geometric constraints VSLAM (SGC-VSLAM), which is built on the RGB-D mode of ORB-SLAM2 with the addition of dynamic detection and static point cloud map construction modules. In detail, a novel improved quadtree-based method was adopted for SGC-VSLAM to enhance the performance of the feature extractor in ORB-SLAM (Oriented FAST and Rotated BRIEF-SLAM). Moreover, a new dynamic feature detection method called semantic and geometric constraints was proposed, which provided a robust and fast way to filter dynamic features. The semantic bounding box generated by YOLO v3 (You Only Look Once, v3) was used to calculate a more accurate fundamental matrix between adjacent frames, which was then used to filter all of the truly dynamic features. Finally, a static point cloud was estimated by using a new drawing key frame selection strategy. Experiments on the public TUM RGB-D (Red-Green-Blue Depth) dataset were conducted to evaluate the proposed approach. This evaluation revealed that the proposed SGC-VSLAM can effectively improve the positioning accuracy of the ORB-SLAM2 system in high-dynamic scenarios and was also able to build a map with the static parts of the real environment, which has long-term application value for autonomous mobile robots.

scholarly journals Semantic 3D Mapping from Deep Image Segmentation

2021 ◽  
Vol 11 (4) ◽  
pp. 1953
Author(s):  
Francisco Martín ◽  
Fernando González ◽  
José Miguel Guerrero ◽  
Manuel Fernández ◽  
Jonatan Ginés
Keyword(s):  
Point Cloud ◽  
Autonomous Mobile Robots ◽  
Semantic Mapping ◽  
Indoor Environments ◽  
3D Mapping ◽  
Direct Mapping ◽  
Learning Techniques ◽  
3D Camera ◽  
Deep Image ◽  
Boundary Pixel

The perception and identification of visual stimuli from the environment is a fundamental capacity of autonomous mobile robots. Current deep learning techniques make it possible to identify and segment objects of interest in an image. This paper presents a novel algorithm to segment the object’s space from a deep segmentation of an image taken by a 3D camera. The proposed approach solves the boundary pixel problem that appears when a direct mapping from segmented pixels to their correspondence in the point cloud is used. We validate our approach by comparing baseline approaches using real images taken by a 3D camera, showing that our method outperforms their results in terms of accuracy and reliability. As an application of the proposed algorithm, we present a semantic mapping approach for a mobile robot’s indoor environments.

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