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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/33170

Title: Fusion of LiDAR and camera sensor data for environment sensing in driverless vehicles
Authors: De Silva, Varuna
Roche, Jamie
Kondoz, Ahmet
Issue Date: 2018
Publisher: arXiv.org
Citation: DE SILVA, V., ROCHE, J. and KONDOZ, A., 2018. Fusion of LiDAR and camera sensor data for environment sensing in driverless vehicles. arXiv:1710.06230v2
Abstract: Driverless vehicles operate by sensing and perceiving its surrounding environment to make the accurate driving decisions. A combination of several different sensors such as LiDAR, radar, ultrasound sensors and cameras are utilized to sense the surrounding environment of driverless vehicles. The heterogeneous sensors simultaneously capture various physical attributes of the environment. Such multimodality and redundancy of sensing need to be positively utilized for reliable and consistent perception of the environment through sensor data fusion. However, these multimodal sensor data streams are different from each other in many ways, such as temporal and spatial resolution, data format, and geometric alignment. For the subsequent perception algorithms to utilize the diversity offered by multimodal sensing, the data streams need to be spatially, geometrically and temporally aligned with each other. In this paper, we address the problem of fusing the outputs of a Light Detection and Ranging (LiDAR) scanner and a wide-angle monocular image sensor. The outputs of LiDAR scanner and the image sensor are of different spatial resolutions and need to be aligned with each other. A geometrical model is used to spatially align the two sensor outputs, followed by a Gaussian Process (GP) regression based resolution matching algorithm to interpolate the missing data with quantifiable uncertainty. The results indicate that the proposed sensor data fusion framework significantly aids the subsequent perception steps, as illustrated by the performance improvement of a typical free space detection algorithm.
Description: This is an arXiv preprint. It can be found at: https://arxiv.org/abs/1710.06230v2
Version: Submitted for publication
URI: https://dspace.lboro.ac.uk/2134/33170
Publisher Link: https://arxiv.org/abs/1710.06230v2
Appears in Collections:Pre-Prints (Loughborough University London)

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