Multiple object detection and classification on uneven terrain using multi-channel lidar for UGV

Abstract

In this paper, we proposed multiple object detection and classification methods using a multi-channel light detection and ranging (LIDAR) device for an unmanned ground vehicle (UGV) in dense forest. The natural terrain environment is described by trees and bushes which are representative wild objects. To describe them on a navigation map, we must detect and classify objects with a single LIDAR sensor rather than multiple sensors on an UGV. It has the advantage of price, communication, and synchronization. If scanning object data are similar with real data, it is better recognizable; a large amount of 3D point cloud data can resemble object in appearance. There is a trade-off relationship between point cloud data and acquisition time. Getting a number of items of point cloud data gradually increases with measurement time and computation time. Our approach for achieving multiple object detection and classification consists of three steps that start from the raw 3D point cloud data. The 3D point cloud data are composed of one frame by one scan. First, the point cloud data are divided by two groups, ground points and non-ground points. Second, the non-ground points are separated by vegetation points and object points. Finally, the object points are arranged as a set of feasible objects with a clustering method. In our approach, random sample consensus (RANSAC), support vector machine (SVM) with principle component analysis (PCA) and fuzzy c-mean (FCM) algorithms belong to each step to derive multiple objects detection and classification. In a local statistic approach, the density of a point cloud in the processing area influences the system performance. Our primary research goal is to achieve improved performance with a data set of one frame per scan for an UGV. Thus, we evaluate the proposed object detection and classification algorithm with a single scan frame which is acquired from three different LIDAR devices. The device has different scan channels. The 1-channel and 4-channel LIDAR devices are commercial devices, LMS-111 and LD-MRS. The 8-channel device, KIDAR-B25, is developed from our previous research with the collaboration of a Korean company. We verify our proposed method through three results: the object detection rate, the point classification rate and the computing time. The results of the method are analyzed with hand-labeled data.