Ground segmentation and free space estimation in off-road terrain

Hamandi, Mahmoud; Asmar, Daniel; Shammas, Elie

doi:10.1016/j.patrec.2018.02.019

Cited by 9 publications

(7 citation statements)

References 7 publications

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“…Recently, various studies on autonomous vehicles have been conducted in various fields. There are papers on various signal processing methods of LiDAR sensors mainly used in autonomous vehicles [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ]. Among them, there are many papers that presented research on free space sensing.…”

Section: Introductionmentioning

confidence: 99%

“…Free space detection is being studied not only for the study of autonomous vehicle driving, but also in various fields such as the recognition of a parking space or the driving of an aircraft [ 25 , 26 , 27 ]. Among them, the research trends in the detection of free space in autonomous vehicles are as follows [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 ]. Often, a camera or LiDAR sensor is used, or both sensors are used, to recognize the space and obstacles around the vehicle.…”

Section: Introductionmentioning

confidence: 99%

“…J. Tao [ 3 ] used LiDAR sensor data to detect free space, in which free space was also detected without information about the speed of the obstacle. Many previous studies used static data without considering the speed of obstacles [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 ]. Free space detection for autonomous driving must also be considered in dynamic environments.…”

Section: Introductionmentioning

confidence: 99%

“…Table 1 summarizes existing papers [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 ]. In previous papers, sensors were used in the FSD process, and the speed information of obstacles was summarized.…”

Section: Introductionmentioning

confidence: 99%

“…In previous papers, sensors were used in the FSD process, and the speed information of obstacles was summarized. In [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 ], obstacles were mainly recognized using LiDAR or camera sensors, and all of them detected free space without information on the velocity of the obstacles.…”

Section: Introductionmentioning

confidence: 99%

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Free Space Detection Algorithm Using Object Tracking for Autonomous Vehicles

Lee

You

2021

Sensors

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In this paper, we propose a new free space detection algorithm for autonomous vehicle driving. Previous free space detection algorithms often use only the location information of every frame, without information on the speed of the obstacle. In this case, there is a possibility of creating an inefficient path because the behavior of the obstacle cannot be predicted. In order to compensate for the shortcomings of the previous algorithm, the proposed algorithm uses the speed information of the obstacle. Through object tracking, the dynamic behavior of obstacles around the vehicle is identified and predicted, and free space is detected based on this. In the free space, it is possible to classify an area in which driving is possible and an area in which it is not possible, and a route is created according to the classification result. By comparing and evaluating the path generated by the previous algorithm and the path generated by the proposed algorithm, it is confirmed that the proposed algorithm is more efficient in generating the vehicle driving path.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Free Space Detection Algorithm Using Object Tracking for Autonomous Vehicles

Lee

You

2021

Sensors

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Traversability analysis for autonomous driving in complex environment: A LiDAR‐based terrain modeling approach

Xue

Liang³

et al. 2023

Journal of Field Robotics

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For autonomous driving, traversability analysis is one of the most basic and essential tasks. In this paper, we propose a novel LiDAR‐based terrain modeling approach, which could output stable, complete, and accurate terrain models and traversability analysis results. As terrain is an inherent property of the environment that does not change with different view angles, our approach adopts a multiframe information fusion strategy for terrain modeling. Specifically, a normal distributions transform mapping approach is adopted to accurately model the terrain by fusing information from consecutive LiDAR frames. Then the spatial‐temporal Bayesian generalized kernel inference and bilateral filtering are utilized to promote the stability and completeness of the results while simultaneously retaining the sharp terrain edges. Based on the terrain modeling results, the traversability of each region is obtained by performing geometric connectivity analysis between neighboring terrain regions. Experimental results show that the proposed method could run in real‐time and outperforms state‐of‐the‐art approaches.

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Traversability Learning from Aerial Images with Fully Convolutional Neural Networks

Braga Borges,

Sá Junior

2023

Neural Process Lett

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Ground segmentation and free space estimation in off-road terrain

Cited by 9 publications

References 7 publications

Free Space Detection Algorithm Using Object Tracking for Autonomous Vehicles

Free Space Detection Algorithm Using Object Tracking for Autonomous Vehicles

Traversability analysis for autonomous driving in complex environment: A LiDAR‐based terrain modeling approach

Traversability Learning from Aerial Images with Fully Convolutional Neural Networks

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