Obstacle Recognition and Traversability Analysis of Tracked Vehicles in Off-road Environment

Haodong WANG; Biao MA; Man CHEN; Liang YU; Yunlu TAN; Yujian LIU

doi:10.12382/bgxb.2024.0981

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Obstacle Recognition and Traversability Analysis of Tracked Vehicles in Off-road Environment

更新时间：2025-10-21

- Obstacle Recognition and Traversability Analysis of Tracked Vehicles in Off-road Environment
- Acta Armamentarii Vol. 46, Issue 9, Pages: 240981(2025)
- 作者机构：
  
  1. 北京理工大学机械与车辆学院, 北京 100081
  2. 中兵智能创新研究院有限公司智能系统总部, 北京 100071
- 作者简介：
- 基金信息：
- DOI：10.12382/bgxb.2024.0981
  CLC：
- Received：22 October 2024，
  
  Published Online：24 September 2025，
  
  Published：30 September 2025
- 稿件说明：
移动端阅览
Haodong WANG, Biao MA, Man CHEN, et al. Obstacle Recognition and Traversability Analysis of Tracked Vehicles in Off-road Environment[J]. Acta Armamentarii, 2025, 46(9): 240981.
DOI：

Haodong WANG, Biao MA, Man CHEN, et al. Obstacle Recognition and Traversability Analysis of Tracked Vehicles in Off-road Environment[J]. Acta Armamentarii, 2025, 46(9): 240981. DOI： 10.12382/bgxb.2024.0981.

摘要

针对现有可通行性分析方法对复杂越野环境下障碍物识别不完整、泛化性差的问题

提出基于开放词汇语义分割的障碍识别算法

可提取车辆周围环境中障碍物和地形的语义标签

能有效识别复杂越野环境下的未知障碍物

在数据集和实车环境中验证了其具备稳定、全面的障碍识别能力。在此基础上

利用语义标签和三维点云构建了多层2.5D地图

基于语义标签对地形的可通行等级进行初步分级;其次基于地面高程计算了地形平整度分数

测量特殊环境要素(如垂直墙)的几何参数

并结合履带车辆几何构型预测了车辆行驶位姿

量化由车辆坡道静态稳定性、地面语义标签和几何属性之间的耦合关系

进而通过代价函数综合评估车辆通行风险和代价

构建以车辆为中心的可通行性地图。通过与同类方法比较验证了所提方法的有效性和可靠性

提升了为无人履带平台的决策、规划和控制提供数据支持。

Abstract

To address the limitations of existing traversability analysis methods

which often suffer from incomplete obstacle recognition and poor generalization in complex off-road environments

this paper proposes an obstacle recognition method based on open-vocabulary semantic segmentation.The method extracts the semantic labels of obstacles and terrain around the vehicle

enabling effective identification of previously unseen obstacles in unstructured environments.The method is validated on the datasets in real-world experiments

demonstrating its stability and comprehensive recognition capability.On this basis

a multi-layer 2.5D map is constructed by integrating semantic labels with 3D point clouds.The traversability level of a terrain is preliminarily classified according to semantic labels.And then the terrain smoothness is quantified based on ground elevation

and the geometric parameters of special environmental features (e.g.

vertical wall) are measured.Furthermore

the driving posture of vehicle is predicted by incorporating the geometric configuration of a tracked vehicle

thereby quantifying the coupling relationship among static slope stability

semantic terrain categories and geometric attributes.A cost function is then designed to jointly assess the traversal risk and cost of vehicle

ultimately generating a vehicle-centric traversability map.The effectiveness and reliability of the proposed method are verified by comparing it with similar methods

which enhances data support for decision-making

planning

and control of unmanned tracked platforms.

关键词

Keywords

references

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