Fast Connected Components Object Segmentation on Fused Lidar and Stereo-Camera Point Clouds with Visual-Inertial-Gimbal for Mobile Applications Utilizing GPU Acceleration

2021 ◽  
pp. 73-83
Author(s):  
Martin Hünermund ◽  
Maik Groneberg ◽  
Artur Schütz
Keyword(s):  
Mobile Applications ◽  
Object Segmentation ◽  
Point Clouds ◽  
Gpu Acceleration ◽  
Connected Components ◽  
Stereo Camera

scholarly journals Boundary-Aware graph Markov neural network for semiautomated object segmentation from point clouds

2021 ◽  
Vol 104 ◽  
pp. 102564
Author(s):  
Huan Luo ◽  
Quan Zheng ◽  
Lina Fang ◽  
Yingya Guo ◽  
Wenzhong Guo ◽  
...  
Keyword(s):  
Neural Network ◽  
Object Segmentation ◽  
Point Clouds

Hybrid obstacle avoidance system with vision and ultrasonic sensors for multi-rotor MAVs

2018 ◽  
Vol 45 (2) ◽  
pp. 227-236 ◽  
Author(s):  
Qiang Zhou ◽  
Danping Zou ◽  
Peilin Liu
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Moving Objects ◽  
Point Clouds ◽  
Ultrasonic Sensor ◽  
Content Type ◽  
Stereo Camera ◽  
3D Point Clouds ◽  
Planning Algorithm ◽  
Path Planning Algorithm

Purpose This paper aims to develop an obstacle avoidance system for a multi-rotor micro aerial vehicle (MAV) that flies in indoor environments which usually contain transparent, texture-less or moving objects. Design/methodology/approach The system adopts a combination of a stereo camera and an ultrasonic sensor to detect obstacles and extracts three-dimensional (3D) point clouds. The obstacle map is built on a coarse global map and updated by local maps generated by the recent 3D point clouds. An efficient layered A* path planning algorithm is also proposed to address the path planning in 3D space for MAVs. Findings The authors conducted a lot of experiments in both static and dynamic scenes. The results show that the obstacle avoidance system works reliably even when transparent or texture-less obstacles are present. The layered A* path planning algorithm is much faster than the traditional 3D algorithm and makes the system response quickly when the obstacle map has been changed because of the moving objects. Research limitations/implications The limited field of view of both stereo camera and ultrasonic sensor makes the system need to change heading first before moving side to side or moving backward. But this problem could be addressed when multiple systems are mounted toward different directions on the MAV. Practical implications The developed approach could be valuable to applications in indoors. Originality/value This paper presents a robust obstacle avoidance system and a fast layered path planning algorithm that are easy to be implemented for practical systems.

scholarly journals POLE-LIKE STREET FURNITURE DECOMPOSTION IN MOBILE LASER SCANNING DATA

2016 ◽  
Vol III-3 ◽  
pp. 193-200
Author(s):  
F. Li ◽  
S. Oude Elberink ◽  
G. Vosselman
Keyword(s):  
Prior Knowledge ◽  
Decomposition Method ◽  
Laser Scanning ◽  
Point Clouds ◽  
Semantic Interpretation ◽  
Connected Components ◽  
Components Separation ◽  
Traffic Signs ◽  
Mixed Classes ◽  
Street Furniture

Automatic semantic interpretation of street furniture has become a popular topic in recent years. Current studies detect street furniture as connected components of points above the street level. Street furniture classification based on properties of such components suffers from large intra class variability of shapes and cannot deal with mixed classes like traffic signs attached to light poles. In this paper, we focus on the decomposition of point clouds of pole-like street furniture. A novel street furniture decomposition method is proposed, which consists of three steps: (i) acquirement of prior-knowledge, (ii) pole extraction, (iii) components separation. For the pole extraction, a novel global pole extraction approach is proposed to handle 3 different cases of street furniture. In the evaluation of results, which involves the decomposition of 27 different instances of street furniture, we demonstrate that our method decomposes mixed classes street furniture into poles and different components with respect to different functionalities.

scholarly journals POLE-LIKE STREET FURNITURE DECOMPOSTION IN MOBILE LASER SCANNING DATA

2016 ◽  
Vol III-3 ◽  
pp. 193-200 ◽  
Author(s):  
F. Li ◽  
S. Oude Elberink ◽  
G. Vosselman
Keyword(s):  
Prior Knowledge ◽  
Decomposition Method ◽  
Laser Scanning ◽  
Point Clouds ◽  
Semantic Interpretation ◽  
Connected Components ◽  
Components Separation ◽  
Traffic Signs ◽  
Mixed Classes ◽  
Street Furniture

Automatic semantic interpretation of street furniture has become a popular topic in recent years. Current studies detect street furniture as connected components of points above the street level. Street furniture classification based on properties of such components suffers from large intra class variability of shapes and cannot deal with mixed classes like traffic signs attached to light poles. In this paper, we focus on the decomposition of point clouds of pole-like street furniture. A novel street furniture decomposition method is proposed, which consists of three steps: (i) acquirement of prior-knowledge, (ii) pole extraction, (iii) components separation. For the pole extraction, a novel global pole extraction approach is proposed to handle 3 different cases of street furniture. In the evaluation of results, which involves the decomposition of 27 different instances of street furniture, we demonstrate that our method decomposes mixed classes street furniture into poles and different components with respect to different functionalities.

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