Obstacle Detection in Hybrid Cross-Country Environment Based on Markov Random Field for Unmanned Ground Vehicle

Feng Ding,Mingheng Zhang,Yibing Zhao,Linhui Li,Lie Guo

doi:10.1155/2015/540968

Feng Ding, Mingheng Zhang + Show 3 more

Open Access

https://doi.org/10.1155/2015/540968

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Abstract

In order to detect the obstacle from the large amount of 3D LIDAR data in hybrid cross-country environment for unmanned ground vehicle, a new graph approach based on Markov random field was presented. Firstly, the preprocessing method based on the maximum blurred line is applied to segment the projection of every laser scan line inx-yplane. Then, based onK-means clustering algorithm, the same properties of the line are combined. Secondly, line segment nodes are precisely positioned by using corner detection method, and the next step is to take advantage of line segment nodes to build an undirected graph for Markov random field. Lastly, the energy function is calculated by means of analyzing line segment features and solved by graph cut. Two types of line mark are finally classified into two categories: ground and obstacle. Experiments prove the feasibility of the approach and show that it has better performance and runs in real time.

Highlights

Environmental awareness is the key point for unmanned ground vehicle
(a) CCD picture in the same scene (b) Laser radar results of manual marking in the same scene
This paper puts forward the obstacle detection algorithm based on laser radar data of three dimensions in the hybrid cross-country environment

Summary

Introduction

Environmental awareness is the key point for unmanned ground vehicle. Obstacle detection is a critical perception-requirement for UGV autonomous navigation. In order to ensure the safety of driving, autonomous vehicles are usually equipped with sensors including camera, laser radar, and microwave sensor. All of those are used to detect obstacles and the ground area [1, 2]. Whose vertical displacement exceeds a given threshold [10] This algorithm can be used to detect large obstacles such as pedestrians, signposts, and cars. Powers and Davis observe that the point-cloud images can be used to identify objects based on combined spatial and spectral features in three dimensions and at long standoff range. The analysis of line segment feature aims to establish the energy function and get the global optimum solution by using the image segmentation algorithm

Summary on Algorithm

The Clustering Segmentation of Scan Line Projection

Corner Detection of Between-Cluster Line Segment

Construct the Undirected Graph on the Markov Random Field

Experiments and Results Analysis

Conclusion