An Adaptive Approach for Road Boundary Detection Using 2D LIDAR Sensor

Date

2017-11-18

Author

Demir, S. O.
Ertop, T. E.
Koku, Ahmet Buğra
Konukseven, Erhan İlhan

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Robotic ground vehicles are commonly used in various road conditions for performing special tasks with different levels of autonomy. This requires the robots to possess a certain degree of perception about the road conditions up ahead and make plans accordingly. It is essential for the robots to be able to quickly adapt their perception for various road conditions they operate. In this paper, an adaptive method for road boundary extraction using 2D LIDAR sensor is presented. A three-stage detection algorithm is utilized for road determination, in which parameter sets are updated adaptively based on a discriminative learning approach. Details of both the learning approach and the detection algorithm are discussed in detail. Experiments were performed on constructed and unconstructed roads to evaluate the performance of the proposed method, and the outcomes were presented in the paper. The results showed that accuracy of road boundary detection increased significantly with the proposed adaptive method. Likewise, significant changes were observed in the required computational times.

Subject Keywords

Roads, Mobile communication, Cameras, Detection algorithms, Robot sensing systems, Laser radar

URI

https://hdl.handle.net/11511/37443

DOI

https://doi.org/10.1109/mfi.2017.8170430

Conference Name

IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)

Collections

Department of Mechanical Engineering, Conference / Seminar

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Citation Formats

S. O. Demir, T. E. Ertop, A. B. Koku, and E. İ. Konukseven, “An Adaptive Approach for Road Boundary Detection Using 2D LIDAR Sensor,” presented at the IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), Daegu, South Korea, 2017, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/37443.