Abstract
Improving the effectiveness of spatial shape features classification from 3D lidar data is very relevant because it is largely used as a fundamental step towards higher level scene understanding challenges of autonomous vehicles and terrestrial robots. In this sense, computing neighborhood for points in dense scans becomes a costly process for both training and classification. This paper proposes a new general framework for implementing and comparing different supervised learning classifiers with a simple voxel-based neighborhood computation where points in each non-overlapping voxel in a regular grid are assigned to the same class by considering features within a support region defined by the voxel itself. The contribution provides offline training and online classification procedures as well as five alternative feature vector definitions based on principal component analysis for scatter, tubular and planar shapes. Moreover, the feasibility of this approach is evaluated by implementing a neural network (NN) method previously proposed by the authors as well as three other supervised learning classifiers found in scene processing methods: support vector machines (SVM), Gaussian processes (GP), and Gaussian mixture models (GMM). A comparative performance analysis is presented using real point clouds from both natural and urban environments and two different 3D rangefinders (a tilting Hokuyo UTM-30LX and a Riegl). Classification performance metrics and processing time measurements confirm the benefits of the NN classifier and the feasibility of voxel-based neighborhood.
Highlights
Three-dimensional (3D) lidar sensors are a key technology for navigation, localization, mapping and scene understanding in novel ground vehicle systems such as autonomous cars [1], search and rescue robots [2], and planetary exploration rovers [3]
In order to reduce the computational load of the neural network (NN) classifier in [30], we implemented a computationally simple voxel-based neighborhood approach where all points in each non-overlapping voxel in a regular grid were assigned to the same class by considering features within a support region defined only by the voxel itself
We evaluate the feasibility of the voxel-based neighborhood concept for classification of terrestrial scene scans by implementing our NN method and three other classifiers commonly found in scene classification applications: support vector machines (SVM), Gaussian processes (GP), and Gaussian mixture models (GMM)
Summary
Three-dimensional (3D) lidar sensors are a key technology for navigation, localization, mapping and scene understanding in novel ground vehicle systems such as autonomous cars [1], search and rescue robots [2], and planetary exploration rovers [3]. Algorithms have been proposed to identify particular object types, such as vehicles, buildings or trees [9,10], or to classify geometric primitives at point level [11] In this sense, while some methods segment the cloud before classifying points within the resulting clusters [12,13], others perform classification directly on scan points [8]. In order to reduce the computational load of the NN classifier in [30], we implemented a computationally simple voxel-based neighborhood approach where all points in each non-overlapping voxel in a regular grid were assigned to the same class by considering features within a support region defined only by the voxel itself.
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