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Open AccessArticle

Multi-Stage Hough Space Calculation for Lane Markings Detection via IMU and Vision Fusion

The College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
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Author to whom correspondence should be addressed.
Sensors 2019, 19(10), 2305; https://doi.org/10.3390/s19102305
Received: 13 April 2019 / Revised: 11 May 2019 / Accepted: 14 May 2019 / Published: 19 May 2019
(This article belongs to the Special Issue Intelligent Vehicles)
It is challenging to achieve robust lane detection based on a single frame, particularly when complicated driving scenarios are present. A novel approach based on multiple frames is proposed in this paper by taking advantage of the fusion of vision and Inertial Measurement Units (IMU). Hough space is employed as a storage medium where lane markings can be stored and visited conveniently. The detection of lane markings is achieved by the following steps. Firstly, primary line segments are extracted from a basic Hough space, which is calculated by Hough Transform. Secondly, a CNN-based classifier is introduced to measure the confidence probability of each line segment, and transforms the basic Hough space into a probabilistic Hough space. In the third step, pose information provided by the IMU is applied to align previous probabilistic Hough spaces to the current one and a filtered probabilistic Hough space is acquired by smoothing the primary probabilistic Hough space across frames. Finally, valid line segments with probability higher than 0.7 are extracted from the filtered probabilistic Hough space. The proposed approach is applied experimentally, and the results demonstrate a satisfying performance compared to various existing methods.
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Keywords: IMU; vision; classification networks; Hough transform; lane markings detection IMU; vision; classification networks; Hough transform; lane markings detection
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Sun, Y.; Li, J.; Sun, Z. Multi-Stage Hough Space Calculation for Lane Markings Detection via IMU and Vision Fusion. Sensors 2019, 19, 2305.

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