Road Traversability Analysis of Unmanned Tracked Platform in Off-road Environment

doi:10.12382/bgxb.2021.0824

Abstract

Abstract: To deal with the problems of fuzzy road features and complex terrain in off-road environment, a road traversability analysis method for unmanned tracked platforms is proposed based on the fusion scheme of camera and lidar for sensing. Firstly, the semantic point cloud is obtained based on image semantic segmentation, and the passable area is roughly extracted. Then the three-dimensional point cloud is used to describe the ground geometric features, and road traversability is analyzed considering the trafficability constraints of the tracked platform. Finally, a three-dimensional traversable grid map containing road surface attributes and ground geometry information is generated. The definition of platform traversability in the proposed method reflects the strong coupling relationship between the platform and the environment. The experimental results show that the algorithm can stably establish the traversable map, provide good guidance for platform planning and control, and is conducive to the stable trafficability of the unmanned tracked platform in the complex off-road environment.

Key words: unmannedtrackplatform, traversabilityanalysis, semanticsegmentation, pointcloudcharacteristics, 3Dgridmap

CLC Number:

TP249

ZHOU Mengru， CHEN Huiyan， XIONG Guangming， GUAN Haijie， LIU Qingxiao. Road Traversability Analysis of Unmanned Tracked Platform in Off-road Environment[J]. Acta Armamentarii, 2022, 43(10): 2485-2496.

References

［1］陈慧岩, 张玉. 军用地面无人机动平台技术发展综述[J]. 兵工学报, 2014, 35(10): 1696-1706.
CHEN H Y, ZHANG Y. An overview of research on military unmanned ground vehicles[J].Acta Armamentarii, 2014, 35(10): 1696-1706. (in Chinese)
[2］赵梓烨, 刘海鸥, 陈慧岩. 分布式电驱动无人高速履带车辆越野环境轨迹预测方法研究[J]. 兵工学报, 2019, 40(4): 680-688.
ZHAO Z Y, LIU H O, CHEN H Y. Research on trajectory prediction method of distributed high speed electric drive unmanned tracked vehicle in off-road conditions[J]. Acta Armamentarii, 2019, 40(4): 680-688. (in Chinese)
[3］袁夏, 赵春霞. 一种激光雷达可通行区域提取算法[J]. 兵工学报, 2010, 31(12): 1702-1707.
YUAN X, ZHAO C X. Traversable area extraction using LADAR sensor[J]. Acta Armamentarii, 2010, 31(12): 1702-1707. (in Chinese)
[4］刘忠泽, 陈慧岩, 崔星, 等. 无人平台越野环境下同步定位与地图创建[J]. 兵工学报, 2019, 40(12): 2399-2406.
LIU Z Z, CHEN H Y, CUI X, et al. Real-time LiDAR SLAM inoff-road Environment for UGV[J]. Acta Armamentarii, 2019, 40(12): 2399-2406. (in Chinese)
[5］黄庭鸿, 聂卓赟, 王庆国,等. 基于区块自适应特征融合的图像实时语义分割[J]. 自动化学报, 2021, 47(5): 1137-1148.
HUANG T H, NIE Z Y, WANG Q G,et al. Real-time image semantic segmentation based on block adaptive feature fusion[J]. Acta Automatica Sinica, 2021, 47(5): 1137-1148. (in Chinese)
[6］ SHARMA S, BALL J E, TANG B, et al. Semantic segmentation with transfer learning for off-road autonomous driving[J]. Sensors, 2019, 19(11): 2577.
[7］ ZHAO X, VEMULAPALLI R, MANSFIELD P A, et al.
Contrastive learning for label efficient semantic segmentation[C]∥
Proceedings of the IEEE/CVF International Conference on Computer Vision.Montreal, Canada:Computer Vision Foundation，2021: 10623-10633.
[8］胡家铭, 胡宇辉, 陈慧岩, 等. 基于模型预测控制的无人驾驶履带车辆轨迹跟踪方法研究[J]. 兵工学报, 2019, 40(3): 456-463.
HU J M, HU Y H, CHEN H Y, et al. Research on trajectory tracking of unmanned tracked vehicles based on model predictive control[J]. Acta Armamentarii, 2019, 40(3): 456-463. (in Chinese)
[9］ CHEN Z, ZHANG J, TAI D C. Progressive LiDAR adaptation for road detection[J]. IEEE/CAA Journal of Automatica Sinica, 2019, 6(3): 693-702.
[10］ MATURANA D, CHOU P W, UENOYAMA M, et al. Real-time semantic mapping for autonomous off-road navigation[M]∥Field and Service Robotics.Berlin,Germany:Springer, 2018: 335-350.
[11］ ZHAO H S, SHI J P, QI X J, et al. Pyramid scene parsing network[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Honolulu, HI,US:IEEE,2017: 2881-2890.
[12］ CHEN L C, ZHU Y, PAPANDREOU G, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[C]∥Proceedings of the European Conference on Computer Vision.Ithaca,NY,US:Cornell University,2018: 801-818.
[13］ BADRINARAYANAN V, KENDALL A, CIPOLLA R. Segnet: a deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(12): 2481-2495.
[14］ WANG J D, SUN K, CHENG T H, et al. Deep high-resolution representation learning for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020,43(10):3349-3364.
[15］ YU C Q, GAO C X, WANG J B, et al. Bisenet v2: bilateral network with guided aggregation for real-time semantic segmentation[J]. International Journal of Computer Vision, 2021, 129(11): 3051-3068.
[16］ BELLONE M, REINA G, GIANNOCCARO N I, et al. 3D traversability awareness for rough terrain mobile robots[J]. Sensor Review, 2014, 34(2): 220-232.
[17］朱余清, 洪添胜, 吴伟斌, 等. 山地果园自走式履带运输车抗侧翻设计与仿真[J]. 农业机械学报, 2012, 43(增刊1): 19-23.
ZHU Y Q, HONG T S, WU W B,et al. Design and simulation of side rollover resistant capability of tracked vehicle for mountain orchard[J]. Transactions of the Chinese Society for Agricultural Machinery, 2012, 43(S1): 19-23. (in Chinese)
[18］ GAN L, ZHANG R, GRIZZLE J W, et al. Bayesian spatial kernel smoothing for scalable dense semantic mapping[J]. IEEE Robotics and Automation Letters, 2020, 5(2): 790-797.
[19］ JI K J, CHEN H Y, DI H J, et al. CPFG-SLAM: a robust simultaneous localization and mapping based on LIDAR in off-road environment[C]∥Proceedings of 2018 IEEE Intelligent Vehicles Symposium(IV). Changshu, China:IEEE, 2018: 650-655.
[20］黄书昊. 非结构化道路无人驾驶车辆可通行区域检测[D].北京：北京理工大学, 2021.
HUANG S H. Drivable area detection for unmanned ground vehicle on unstructured roads[D].Beijing:Beijing Institute of Technology, 2021.(in Chinese)
[21］ ZHU B C, XIONG G M, DI H J, et al. A novel method of traversable area extraction fused with LiDAR odometry in off-road environment[C]∥Proceedings of 2019 IEEE International Conference on Vehicular Electronics and Safety (ICVES). Cairo, Egypt:IEEE, 2019: 1-6.