Real time obstacle detection method based on lidar and wireless sensor

Zhang, Junyou; Han, Jian; Wang, Shufeng; Liao, Yaping Joyce; Li, Pengfei

doi:10.1109/cac.2017.8243848

Cited by 8 publications

(6 citation statements)

References 8 publications

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“…The experimental results are shown in table 1-3. The experimental results show that under the same scenario, the average positive detection rates of the proposed algorithm, the methods in literature [9] and literature [14] are 86.9%, 83.8% and 84.3% respectively, which are improved compared with the traditional algorithms. The method in reference [14] has poor practicability on the vehicle platform because the average time is too large and exceeds the sampling frequency of the lidar.…”

Section: Analysis and Discussion Of Experimental Resultsmentioning

confidence: 85%

“…During the experiment, continuous frame data packets were also tested. The traditional Euclidean clustering algorithm, the detection algorithm in literature [9], literature [14] and the algorithm in this paper were used to detect and identify the data in the same time period. The experimental results are shown in table 1-3.…”

Section: Analysis and Discussion Of Experimental Resultsmentioning

confidence: 99%

“…Add the points with the distance less than the dynamic clustering search radius r to the cluster of the target points, otherwise eliminate them until the number of point clouds in the cluster is stable, and then complete the clustering. The point cloud data in three-dimensional space is composed of a large number of discrete points, which is in a scattered and disordered state, and does not have the geometric topological relationship in traditional grid data [13] , which makes a lot of time consumed in data search during point cloud clustering [14] . KD-tree is an index tree structure specially used to search large-scale high-dimensional data, which evolved from binary tree [15] .…”

Section: Improved Euclidean Clustering Algorithmmentioning

confidence: 99%

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Point cloud target detection method combining spectral clustering and improved Euclidean clustering

Hou

Luo²,

Wang³

2023

Third International Conference on Computer Vision and Data Mining (ICCVDM 2022)

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With the development of unmanned sensing technology, using lidar sensor to obtain 3D information of obstacles has become a research hotspot. Because the laser point cloud has the characteristics of near density and far sparse, it is easy to make the phenomenon of under segmentation of nearby objects and missing detection of distant objects in target detection, which is very easy to produce inaccurate detection results. In order to solve this problem, a combination of spectral clustering and improved European clustering algorithm is proposed, which effectively solves the problems of under segmentation and missing detection. In the experiment, the clustering effect of this algorithm is compared with the traditional European clustering algorithm and the latest point cloud detection algorithm. The real vehicle experimental results show that the average positive detection rate of the proposed algorithm is 86.9%, which is improved compared with other methods. The average time of the algorithm is 87ms, which is practical on the real vehicle platform.

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Section: Analysis and Discussion Of Experimental Resultsmentioning

confidence: 85%

Section: Analysis and Discussion Of Experimental Resultsmentioning

confidence: 99%

Section: Improved Euclidean Clustering Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Point cloud target detection method combining spectral clustering and improved Euclidean clustering

Hou

Luo²,

Wang³

2023

Third International Conference on Computer Vision and Data Mining (ICCVDM 2022)

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“…Object classification in an autonomous vehicle has been presented in [10] based on point cloud results from LiDAR data obtained using convolution neural networks. Further, work presented in [11]- [13] also demonstrate obstacle avoidance and detection from 2D and 3D LiDAR data respectively.…”

Section: Literature Reviewmentioning

confidence: 91%

LiDAR-based Obstacle Detection and Avoidance for Autonomous Vehicles using Raspberry Pi 3B

Calcroft

Khan

2022

2022 UKACC 13th International Conference on Control (CONTROL)

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Autonomous vehicles are redefining the transport industry -obstacle detection and avoidance are key to their operation. A number of sensor technologies have been developed and trialled. This paper presents the implementation of a Hokuyo URG-04LX Light Detection And Ranging (LiDAR) sensor on an autonomous vehicle developed with a Raspberry Pi 3B microcontroller and demonstrates its effectiveness for object detection and avoidance in varying conditions. The LiDAR sensor was integrated with the Raspberry Pi 3B using Python on LUbuntu (lightweight version of Ubuntu) and Robot Operating System (ROS). Various scenarios with low light conditions, reflective surfaces at multiple angles, simple stopping tests and different motion paths at varying speeds were tested. All tests were run at 3.2 and 4mph speed. It was found that the LiDAR sensor performed well for basic object detection but did not respond well to reflective or dark surfaces. We further compared the LiDAR's performance with ultrasonic sensors and found that it outperformed ultrasonic sensors for stopping distances. Overall, the LiDAR acts as an effective sensor for the autonomous vehicle, showing its viability in detecting objects and acting as a small scale representation of autonomous technology.

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“…In [5], a more sophisticated road boundary and obstacle detection scheme has been used using a downward-looking LiDAR sensor. Another study [6] presents a LiDAR and wireless sensor-based real time obstacle detection method.…”

Section: Lidar Sensorsmentioning

confidence: 99%

Obstacle Detection and Track Detection in Autonomous Cars

Iqbal¹

2020

Autonomous Vehicle and Smart Traffic

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This chapter illustrates the history and recent advancements in the field of Autonomous Vehicles with regard to two important concerns that play the most vital role in successful implementation and working of an Autonomous Car: (1) Obstacle Detection and (2) Track Detection. The car should be able to detect the obstacles for smooth and efficient working in order to avoid accident and collision. It should also be able to calculate the distance of the obstacle from the car. Similarly, Track Detection is also important as the autonomous car should stay within a predefined track and has to keep itself within the yellow lines on both sides of the road. This chapter elaborates the technologies and advancements that have been presented in the literature till date that deal with Obstacle Detection and Track Detection in Autonomous Cars/Vehicles.

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Real time obstacle detection method based on lidar and wireless sensor

Cited by 8 publications

References 8 publications

Point cloud target detection method combining spectral clustering and improved Euclidean clustering

Point cloud target detection method combining spectral clustering and improved Euclidean clustering

LiDAR-based Obstacle Detection and Avoidance for Autonomous Vehicles using Raspberry Pi 3B

Obstacle Detection and Track Detection in Autonomous Cars

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