面向无人平台建筑内导航的地图轻量化处理与楼梯区域分类方法

doi:10.12382/bgxb.2024.0483

摘要/Abstract

摘要：

构建环境地图是导航的前提,完整详细的地图能够有效辅助无人平台规划最佳移动路径。针对传统导航地图构建方法存在数据冗余及地形结构区分困难的问题,提出一种面向无人平台建筑内导航的地图轻量化处理与楼梯区域分类方法。提取无人平台可行驶区域,减少数据冗余,并基于楼梯上表面的分布特性去除离群点。利用包含边缘平滑处理的地图构建算法,生成边界清晰、形状规整的多层栅格地图(Multi-layer Grid Map, MGM)。提取楼梯环境特征,结合Laplace平滑的朴素贝叶斯分类算法,区分并标记台阶和转弯平台等结构。实验结果表明:所提方法生成的地图在保持高分辨率的同时,数据量较传统点云地图减少了1个数量级以上,且地图分类的宏精准率达到91.3%。相较于传统方法,该方法能构建更加轻量化且具有地形分类标签的建筑内MGM,为无人平台安全高效的导航提供支持。

关键词: 地图数据处理, 楼梯, 轻量化, 地图分类, 地面无人平台

Abstract:

Constructing an environment map is a crucial prerequisite for navigation,and a comprehensive and detailed map can effectively assist in planning the optimal motion paths for unmanned ground platforms.To address the issues of data redundancy and difficulty in distinguishing the terrain structures in traditional map construction methods,a lightweight map processing and staircase area classification method for indoor navigation of unmanned ground platforms is proposed.The method first extracts the traversable area for unmanned platforms to reduce data redundancy and removes the outliers based on the distribution characteristics of stair surfaces.Subsequently,a map construction algorithm incorporating edge smoothing is used to generate multi-layer grid maps with clear boundaries,regular shapes,and distinct levels.Then,the stair environment features are extracted,and a Naive Bayes classification algorithm with Laplace smoothing is employed to distinguish and label the structures such as steps and turning platforms.The experimental results show that the maps generated by this method maintain high resolution while reducing the data volume by an order of magnitude compared to traditional point cloud maps,and the macro-precision rate of map classification reaches 91.3%.Compared with conventional methods,the proposed method can construct more lightweight multi-layer grid maps with terrain classification labels,providing safe and efficient navigation support for unmanned ground platforms.

Key words: map data processing, staircase, lightweight, map classification, unmanned ground platform

中图分类号:

TP242

马雨薇, 武伟超, 王伟, 牛爱林, 郭志明, 杨建新. 面向无人平台建筑内导航的地图轻量化处理与楼梯区域分类方法[J]. 兵工学报, 2025, 46(6): 240483-.

MA Yuwei, WU Weichao, WANG Wei, NIU Ailin, GUO Zhiming, YANG Jianxin. Map Lightweight Processing and Staircase Area Classification Method for Indoor Navigation of Unmanned Ground Platforms[J]. Acta Armamentarii, 2025, 46(6): 240483-.

图/表 16

图1 地图构建流程

Fig.1 Map construction process

图2 环境法向量分布情况

Fig.2 Distribution of environmental normal vectors

图3 提取后的点云

Fig.3 Extracted point cloud

图4 水平区域几何参数及判断域

Fig.4 Geometric parameters of horizontal regions and decision domain

图5 去除离群点

Fig.5 Diagram of outlier removal

图6 离群点滤波时ε-P关系

Fig.6 ε-P relationship graph for outlier filtering

图7 MGM算法伪代码

Fig.7 Pseudocode for MGM algorithm

图8 Smoothing函数卷积

Fig.8 Schematic diagram of Smoothing function convolution

图9 去除天花板操作结果

Fig.9 Illustration of the results of removing the ceiling operation

图10 地面边缘栅格地图

Fig.10 Ground edge grid map

图11 地面边缘栅格地图统计结果

Fig.11 The statistical results of ground edge grid map

表1 多分类混淆矩阵

Table 1 Confusion matrix for multi-class classification

真实类别	预测类别
真实类别	c₁	c₂	…	c_k
c₁	$∑ i = 1 V I (p i = c 1, r i = c 1)$	$∑ i = 1 V I (p i = c 2, r i = c 1)$	…	$∑ i = 1 V I (p i = c k, r i = c 1)$
c₂	$∑ i = 1 V I (p i = c 1, r i = c 2)$	$∑ i = 1 V I (p i = c 2, r i = c 2)$	…	$∑ i = 1 V I (p i = c k, r i = c 2)$
︙	︙	︙		︙
c_k	$∑ i = 1 V I (p i = c 1, r i = c k)$	$∑ i = 1 V I (p i = c 2, r i = c k)$	…	$∑ i = 1 V I (p i = c k, r i = c k)$

表1 多分类混淆矩阵

Table 1 Confusion matrix for multi-class classification

真实类别	预测类别
真实类别	c₁	c₂	…	c_k
c₁	$∑ i = 1 V I (p i = c 1, r i = c 1)$	$∑ i = 1 V I (p i = c 2, r i = c 1)$	…	$∑ i = 1 V I (p i = c k, r i = c 1)$
c₂	$∑ i = 1 V I (p i = c 1, r i = c 2)$	$∑ i = 1 V I (p i = c 2, r i = c 2)$	…	$∑ i = 1 V I (p i = c k, r i = c 2)$
︙	︙	︙		︙
c_k	$∑ i = 1 V I (p i = c 1, r i = c k)$	$∑ i = 1 V I (p i = c 2, r i = c k)$	…	$∑ i = 1 V I (p i = c k, r i = c k)$

表2 分类结果评价值

Table 2 Classification result evaluation value

评价指标	评价值
P_M	0.913
R_M	0.837
F1_M	0.872

图12 构建MGM实验

Fig.12 Experiment on constructing multi-layer grid map

表3 地图模型参数对比

Table 3 Comparison of parameters of different map models

地图模型	数据集	能否直接用于路径规划	占用存储空间	面向地面无人平台的可通行区域占比/%
点云	S3DIS	否	82111KB
点云	BIT3DIS	否	2263MB
体素	S3DIS	是	4691KB	33.5
体素	BIT3DIS	是	33.5MB	22.0
三维网格	S3DIS	是	7505KB	40.0
三维网格	BIT3DIS	是	47.1MB	39.9
八叉树	S3DIS	是	1519KB	33.6
八叉树	BIT3DIS	是	13.8MB	22.3
MGM	S3DIS	是	3028KB	99.2
MGM	BIT3DIS	是	25MB	96.4

图13 路径规划

Fig.13 Schematic diagram of path planning

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