Abstract
The environment perception of autonomous vehicle is mainly aimed at obtaining the holonomic information around the ego-vehicle to understand the driving environment. In this paper, we attempt to use only vehicle-mounted cameras to reconstruct the 3D environment for the autonomous vehicle perception. Firstly, we acquire the continuous frames in vehicle motion to reconstruct continuous 3D point clouds. Secondly, the continuous point clouds can be stitched by the proposed matching constraint and scores of image features. Lastly, we get high-accuracy and high-efficiency dense point cloud of the environment. Experimental results on the benchmark dataset demonstrate the effectiveness and robustness of the proposed stitching algorithm. Compared with other algorithms and its variants, the MSE of the proposed method is lower than the average and the number of redundant points in overlap regions is reduced.
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References
Cao, M.: Robust bundle adjustment for large-scale structure from motion. Multimedia Tools Appl. 76(21), 1–25 (2017)
Geiger, A., Philip, L., Raquel, U.: Are we ready for autonomous driving? The kitti vision benchmark suite. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2012, pp. 3354–3361. IEEE (2012)
He, Y.: An iterative closest points algorithm for registration of 3D laser scanner point clouds with geometric features. Sensors 17(8), 1862–1872 (2017)
Li, Y.: SIFT keypoint removal and injection via convex relaxation. IEEE Trans. Inf. Forensics Secur. 11(8), 1722–1735 (2016)
Liang, B., Zheng, L.: Specificity and latent correlation learning for action recognition using synthetic multi-view data from depth maps. IEEE Trans. Image Process. 26(12), 5560–5574 (2017)
Mahdi, F.A., Ahmad Fauzi, M.F., Ahmad, N.N.: Image retrieval using most similar highest priority principle based on fusion of colour and texture features. In: Anthony, P., Ishizuka, M., Lukose, D. (eds.) PRICAI 2012. LNCS (LNAI), vol. 7458, pp. 765–770. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32695-0_70
Rublee, E.: ORB: an efficient alternative to SIFT or SURF. In: IEEE international conference on Computer Vision (ICCV) 2011, pp. 2564–2571. IEEE (2011)
Suhr, J., Ho J.: Noise-resilient road surface and free space estimation using dense stereo. In: Intelligent Vehicles Symposium (IV), pp. 461–466. IEEE (2013)
Wang, W., Michael, K.: A variational method for multiple-image blending. IEEE Trans. Image Process. 21(4), 1809–1822 (2012)
Wang, X.: An iterative closest point approach for the registration of volumetric human retina image data obtained by optical coherence tomography. Multimedia Tools Appl. 76(5), 6843–6857 (2017)
Zheng, S.: A multi-frame graph matching algorithm for low-bandwidth RGB-D SLAM. Comput. Aided Des. 7(8), 107–117 (2016)
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Hu, F., Bai, L., Li, Y., Tian, Z. (2018). Environmental Reconstruction for Autonomous Vehicle Based on Image Feature Matching Constraint and Score. In: Geng, X., Kang, BH. (eds) PRICAI 2018: Trends in Artificial Intelligence. PRICAI 2018. Lecture Notes in Computer Science(), vol 11013. Springer, Cham. https://doi.org/10.1007/978-3-319-97310-4_16
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DOI: https://doi.org/10.1007/978-3-319-97310-4_16
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