首页 > 论文 > 光学学报 > 39卷 > 5期(pp:528003--1)

基于核密度估计的城市动态密集场景激光雷达定位

Robust Localization Based on Kernel Density Estimation in Dynamic Diverse City Scenes Using Lidar

  • 摘要
  • 论文信息
  • 参考文献
  • 被引情况
  • PDF全文
分享:

摘要

城市环境中的精确定位是自动驾驶领域的重点和难点, 现有的激光雷达定位算法虽然能够在多数情况下保持较高的精度, 但在一些比较复杂的城市动态场景中仍存在问题。针对这类场景中遮挡导致的全球定位系统定位精度下降, 以及运动目标和环境变化导致的有效点云特征减少的问题, 提出一个新的概率定位框架; 该框架使用核密度估计的方法对改进后的多层次随机采样一致性算法和直方图滤波算法进行融合, 以有效克服多层次随机采样一致性算法在部分场景中的定位波动问题, 以及直方图滤波算法在位姿误差较大时的效率低下和局部最优问题。结果表明:所提框架在保证定位精度的前提下, 提升了对动态密集场景的适用性, 能够在现有算法容易出错的场景中实现更加稳定精确的定位, 并能够容忍更大的初始位姿误差。

Abstract

Achieving high-accuracy localization in urban environments is challenging in autonomous driving. The existing LiDAR-based localization algorithms can ensure high accuracy in most cases; however, the localization problems in complex dynamic city scenes still need to be addressed. This study proposes a novel probabilistic localization framework to mitigate the accuracy degradation of the global positioning system caused by occlusion and to reduce the effective point cloud features caused by moving objects and changing environments in such scenarios. The proposed framework combines the improved multi-layer random sample consensus algorithm and the histogram filtering algorithm with the kernel density estimation method; this combination effectively overcomes the localization fluctuation of multi-layer random sample consensus in some scenes as well as the inefficiency and local optimum of histogram filtering when the pose error is large. The experimental results indicate that the proposed framework can provide more stable and accurate localization as well as tolerate larger initial pose errors compared with the existing localization methods when applied to complex dynamic city scenes.

Newport宣传-MKS新实验室计划
补充资料

中图分类号:TP751.2

DOI:10.3788/aos201939.0528003

所属栏目:遥感与传感器

基金项目:国家重点研发计划(2016YFB0100903)

收稿日期:2018-12-17

修改稿日期:2019-01-06

网络出版日期:2019-01-23

作者单位    点击查看

王任栋:陆军军事交通学院, 天津 300161
李华:陆军军事交通学院, 天津 300161
赵凯:陆军军事交通学院, 天津 300161
徐友春:陆军军事交通学院, 天津 300161

联系人作者:王任栋(1243285824@qq.com); 徐友春(xu56419@126.com);

【1】Lee B H, Song J H, Im J H, et al. GPS/DR error estimation for autonomous vehicle localization[J]. Sensors, 2015, 15(8): 20779-20798.

【2】Levinson J, Montemerlo M, Thrun S. Map-based precision vehicle localization in urban environments[C]//Burgard W, Brock O, Stachniss C. Proceedings of the 3rd Conference on Robotics: Science and Systems, June 27-30, 2007, Georgia Institute of Technology, Atlanta, Georgia, USA. Cambridge: The MIT Press, 2007: 352.

【3】Levinson J, Thrun S. Robust vehicle localization in urban environments using probabilistic maps[C]// Proceedings of IEEE International Conference on Robotics and Automation, May 3-7, 2010, Anchorage, AK, USA. New York: IEEE, 2010: 4372-4378.

【4】Aldibaja M, Suganuma N, Yoneda K. Robust intensity-based localization method for autonomous driving on snow-wet road surface[J]. IEEE Transactions on Industrial Informatics, 2017, 13(5): 2369-2378.

【5】Kim H, Liu B B, Goh C Y, et al. Robust vehicle localization using entropy-weighted particle filter-based data fusion of vertical and road intensity information for a large scale urban area[J]. IEEE Robotics and Automation Letters, 2017, 2(3): 1518-1524.

【6】Wolcott R W, Eustice R M. Fast LIDAR localization using multiresolution Gaussian mixture maps[C]// Proceedings of IEEE International Conference on Robotics and Automation, May 26-30, 2015, Seattle, WA, USA. New York: IEEE, 2015: 2814-2821.

【7】Wolcott R W, Eustice R M. Robust LIDAR localization using multiresolution Gaussian mixture maps for autonomous driving[J]. The International Journal of Robotics Research, 2017, 36(3): 292-319.

【8】Hata A Y, Wolf D F. Feature detection for vehicle localization in urban environments using a multilayer LIDAR[J]. IEEE transactions on Intelligent Transportation Systems, 2016, 17(2): 420-429.

【9】Schlichting A, Brenner C. Vehicle localization by LiDAR point correlation improved by change detection[J]. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016, XLI-B1: 703-710.

【10】Im J H, Im S H, Jee G I. Vertical corner feature based precise vehicle localization using 3D LIDAR in urban area[J]. Sensors, 2016, 16(8): 1268.

【11】Wan G W, Yang X L, Cai R L, et al. Robust and precise vehicle localization based on multi-sensor fusion in diverse city scenes[C]//Proceedings of IEEE International Conference on Robotics and Automation, May 21-25, 2018, Brisbane, QLD, Australia. New York: IEEE, 2018: 4670-4677.

【12】Thrun S. Probabilistic robotics[J]. Communications of the ACM, 2002, 45(3): 52-57.

【13】Doucet A, de Freitas N, Murphy K, et al. Rao-Blackwellised particle filtering for dynamic Bayesian networks[C]//Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence, June 30-July 3, 2000, San Francisco, CA, USA. San Francisco: Morgan Kaufmann Publishers Inc., 2000: 176-183.

【14】Wang R D, Xu Y C, Qi Y, et al. A robust point cloud registration method in urban dynamic environment[J]. Robot, 2018, 40(3): 257-265.
王任栋, 徐友春, 齐尧, 等. 一种鲁棒的城市复杂动态场景点云配准方法[J]. 机器人, 2018, 40(3): 257-265.

【15】Lu W J, Seignez E, Rodriguez F S A, et al. Lane marking based vehicle localization using particle filter and multi-kernel estimation[C]//Proceedings of the 13th International Conference on Control, Automation, Robotics and Vision, December 10-12, 2014, Marina Bay Sands, Singapore. New York: IEEE, 2014: 601-606.

【16】Han D B, Xu Y C, Wang R D, et al. Calibration of three-dimensional lidar extrinsic parameters based on multiple-point clouds matching[J]. Laser & Optoelectronics Progress, 2018, 55(2): 022803.
韩栋斌, 徐友春, 王任栋, 等. 基于多对点云匹配的三维激光雷达外参数标定[J]. 激光与光电子学进展, 2018, 55(2): 022803.

【17】Zhao K, Xu Y C, Li Y L, et al. Large-scale scattered point-cloud denoising based on VG-DBSCAN algorithm[J]. Acta Optica Sinica, 2018, 38(10): 1028001.
赵凯, 徐友春, 李永乐, 等. 基于VG-DBSCAN算法的大场景散乱点云去噪[J]. 光学学报, 2018, 38(10): 1028001.

【18】Xiong F G, Huo W, Han X, et al. Removal method of mismatching keypoints in 3D point cloud[J]. Acta Optica Sinica, 2018, 38(2): 0210003.
熊风光, 霍旺, 韩燮, 等. 三维点云中关键点误匹配剔除方法[J]. 光学学报, 2018, 38(2): 0210003.

引用该论文

Wang Rendong,Li Hua,Zhao Kai,Xu Youchun. Robust Localization Based on Kernel Density Estimation in Dynamic Diverse City Scenes Using Lidar[J]. Acta Optica Sinica, 2019, 39(5): 0528003

王任栋,李华,赵凯,徐友春. 基于核密度估计的城市动态密集场景激光雷达定位[J]. 光学学报, 2019, 39(5): 0528003

您的浏览器不支持PDF插件,请使用最新的(Chrome/Fire Fox等)浏览器.或者您还可以点击此处下载该论文PDF