Dynamic obstacle detection through cooperation of purposive visual modules of color, stereo and motion

2002 ◽  
Author(s):  
Zhigang Zhu ◽  
Guangyou Xu ◽  
Shaoyun Chen ◽  
Xueyin Lin
Keyword(s):  
Obstacle Detection ◽  
Dynamic Obstacle

Unmanned vehicle dynamic obstacle detection, tracking and recognition method based on laser sensor

2021 ◽  
Vol 14 (2) ◽  
pp. 239-251
Author(s):  
Hualei Zhang ◽  
Mohammad Asif Ikbal
Keyword(s):  
Feature Fusion ◽  
Obstacle Detection ◽  
Geometric Features ◽  
Recognition Method ◽  
Content Type ◽  
Detection And Tracking ◽  
Spatio Temporal ◽  
Dynamic Obstacle ◽  
Real Vehicle ◽  
Temporal Feature

PurposeIn response to these shortcomings, this paper proposes a dynamic obstacle detection and tracking method based on multi-feature fusion and a dynamic obstacle recognition method based on spatio-temporal feature vectors.Design/methodology/approachThe existing dynamic obstacle detection and tracking methods based on geometric features have a high false detection rate. The recognition methods based on the geometric features and motion status of dynamic obstacles are greatly affected by distance and scanning angle, and cannot meet the requirements of real traffic scene applications.FindingsFirst, based on the geometric features of dynamic obstacles, the obstacles are considered The echo pulse width feature is used to improve the accuracy of obstacle detection and tracking; second, the space-time feature vector is constructed based on the time dimension and space dimension information of the obstacle, and then the support vector machine method is used to realize the recognition of dynamic obstacles to improve the obstacle The accuracy of object recognition. Finally, the accuracy and effectiveness of the proposed method are verified by real vehicle tests.Originality/valueThe paper proposes a dynamic obstacle detection and tracking method based on multi-feature fusion and a dynamic obstacle recognition method based on spatio-temporal feature vectors. The accuracy and effectiveness of the proposed method are verified by real vehicle tests.

Dynamic Obstacle Detection and Tracking Based on 3D Lidar

2018 ◽  
Vol 22 (5) ◽  
pp. 602-610
Author(s):  
Qixue Zhong ◽  
Yuansheng Liu ◽  
Xiaoxiao Guo ◽  
Lijun Ren ◽  
◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Nearest Neighbor ◽  
Obstacle Detection ◽  
Static State ◽  
Detection And Tracking ◽  
Multiple Hypothesis Tracking ◽  
Tracking Model ◽  
Dynamic Obstacle ◽  
Nearest Neighborhood ◽  
3D Lidar

Detection and tracking of dynamic obstacle is one of the research hotspot in autonomous vehicles. In this paper, a dynamic obstacle detection and tracking method based on 3D lidar is proposed. The nearest neighborhood method is used to cluster the data obtained by the laser lidar. The characteristic parameters of the clustering obstacles are analyzed. Multiple hypothesis tracking model (MHT) algorithm and the nearest neighbor association algorithm are used for data association of two consecutive frames of obstacle information. The dynamic and static state of obstacles are analyzed through the temporal and spatial correlation of the obstacle. Finally, we use linear Kalman filter to predict the movement state of the obstacle. The experimental results on a low-speed driverless vehicle “small whirlwind” which is an autonomous sightseeing vehicle show that the method can accurately detect the dynamic obstacles in unknown environment with effectiveness and real-time performance.

scholarly journals A Hybrid Vision System for Dynamic Obstacle Detection

2018 ◽  
Vol 133 ◽  
pp. 153-160 ◽  
Author(s):  
Kirti Shankar Sharma ◽  
Sudeepta Ranjan Sahoo ◽  
P.V. Manivannan
Keyword(s):  
Vision System ◽  
Obstacle Detection ◽  
Dynamic Obstacle

Obstacle Avoidance Considerations for the REMUS Autonomous Underwater Vehicle

2003 ◽  
Author(s):  
Lynn R. Fodrea ◽  
Anthony J. Healey
Keyword(s):  
Obstacle Avoidance ◽  
Autonomous Underwater Vehicle ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicle ◽  
Gain Factor ◽  
Obstacle Detection ◽  
Dynamic Obstacle Avoidance ◽  
Dynamic Obstacle ◽  
World Mission ◽  
Forward Looking

Future Naval operations necessitate the incorporation of autonomous underwater vehicles into a collaborative network. In future complex missions, a forward look capability will also be required to map and avoid obstacles such as sunken ships. This work examines obstacle avoidance behaviors using a hypothetical forward-looking sonar for the autonomous underwater vehicle REMUS. Hydrodynamic coefficients are used to develop steering equations that model REMUS through a track of specified points similar to a real-world mission track. A two-dimensional forward-looking sonar model with a 120° horizontal scan and a 110 meter radial range is modeled for obstacle detection. Sonar mappings from geographic range-bearing coordinates are developed for implementation in MATLAB simulations. The product of bearing and range weighting functions form the gain factor for a dynamic obstacle avoidance behavior. The overall vehicle heading error incorporates this obstacle avoidance term to develop a path around detected objects. REMUS is a highly responsive vehicle in the model and is capable of avoiding multiple objects in proximity along its track path.

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