Abstract
Abstract. This paper describes a method that aims to find all instances of a certain object in Mobile Laser Scanner (MLS) data. In a userassisted approach, a sample segment of an object is selected, and all similar objects are to be found. By selecting samples from multiple classes, a classification can be performed. Key assumption in this approach is that a one-to-one relationship exists between segments and objects. In this paper the focus is twofold: (1) to explain how to get proper segments, and (2) to describe how to find similar objects. Point attributes that help separating neighbouring objects are presented. These point attributes are used in an attributed connected component algorithm where segments are grown, based on proximity and attribute values. Per component, a feature vector is proposed that consists of two parts. The first is a height histogram, containing information on the height distribution of points within a component. The second contains size and shape information, based on the components’ bounding box. A simple correlation function is used to find similarities between samples, as selected by a user, and other components. Our approach is tested on a MLS dataset, containing over 300 objects in 13 classes. Detection accuracies heavily depend on the success of the segmentation, and the number of selected samples in combination with the variety of object types in the scene.
Highlights
The number of Mobile Mapping Systems (MMS) is increasing rapidly over the past few years
The dataset is acquired by TOPSCAN with a Lynx mobile mapping system
An ICP algorithm can be performed to check whether sample and component are corresponding to similar objects
Summary
The number of Mobile Mapping Systems (MMS) is increasing rapidly over the past few years. Applications can be found in fields related to urban safety analyses and/or asset management. Many MMS systems carry video cameras and laser scanner systems. Mobile laser scanner (MLS) data is a very rich data source for making inventories of the condition and number of objects in an urban environment. The huge amount of 3D points contain information on size, shape and location of objects, and their in-between distances. The problem is to accurately find these objects in a dataset that includes the surrounding area with many other complex shaped objects. Existing methods to classify MLS data can be divided into rule based approaches where the rules describe how the objects of interest look like, and training based methods
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