Abstract
Abstract. In this paper we present an approach for the detection of persons in point clouds gathered by mobile laser scanning (MLS) systems. The approach consists of a preprocessing and the actual detection. The main task of the preprocessing is to reduce the amount of data which has to be processed by the detection. To fulfill this task, the preprocessing consists of ground removal, segmentation and several filters. The detection is based on an implicit shape models (ISM) approach which is an extension to bag-of-words approaches. For this detection method, it is sufficient to work with a small amount of training data. Although in this paper we focus on the detection of persons, our approach is able to detect multiple classes of objects in point clouds. Using a parameterization of the approach which offers a good compromise between detection and runtime performance, we are able to achieve a precision of 0.68 and a recall of 0.76 while having a average runtime of 370 ms per single scan rotation of the rotating head of a typical MLS sensor.
Highlights
In this paper we present an approach for the detection of persons in point clouds gathered by mobile laser scanning (MLS) systems
The detection of pedestrians in data which are gathered by mobile laser scanning (MLS) systems is a useful functionality for several use cases
We have demonstrated that it is possible to detect persons in the 3D point clouds acquired by a MLS system
Summary
The detection of pedestrians (or more general persons) in data which are gathered by mobile laser scanning (MLS) systems is a useful functionality for several use cases It is, for example, helpful for the save navigation of an autonomous vehicle or the save operation of an autonomous machine in the vicinity of people. This is demonstrated, which shows a person in different distances in single scans of a LiDAR sensor which is typically used in MLS systems Another challenge is that many of the use cases depend on real time processing, meaning that the person detection method should be able to process data in the speed of data acquisition.
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