Finding all neighbors of a point inside a given radius is an integral part in many approaches using three-dimensional laser range data. We present novel insights to significantly improve the runtime performance of radius neighbor search using octrees. Our contributions are as follows: (1) We propose an index-based organization of the point cloud such that we can efficiently store start and end indexes of points inside every octant and (2) exploiting this representation, we can use pruning of irrelevant subtrees in the traversal to facilitate highly efficient radius neighbor search. We show significant runtime improvements of our proposed octree representation over state-of-the-art neighbor search implementations on three different urban datasets.

In this paper, we propose a segment-based object detection approach using laser range data. Our detection approach is built up of three stages: First, a hierarchical segmentation approach generates a hierarchy of coarse-to-fine segments to reduce the impact of over- and under-segmentation in later stages. Next, we employ a learned mixture model to classify all segments. The model combines multiple softmax regression classifiers learned on specific bag-of-word representations using different parameterizations of a descriptor. In the final stage, we filter irrelevant and duplicate detections using a greedy method in consideration of the segment hierarchy. We experimentally evaluate our approach on recently published real-world datasets to detect pedestrians, cars, and cyclists.

The selection of suitable features and their parameters for the classification of three-dimensional laser range data is a crucial issue for high-quality results. In this paper we compare the performance of different histogram descriptors and their parameters on three urban datasets recorded with various sensors-sweeping SICK lasers, tilting SICK lasers and a Velodyne 3D laser range scanner. These descriptors are 1D, 2D, and 3D histograms capturing the distribution of normals or points around a query point. We also propose a novel histogram descriptor, which relies on the spectral values in different scales. We argue that choosing a larger support radius and a z-axis based global reference frame/axis can boost the performance of all kinds of investigated classification models significantly. The 3D histograms relying on the point distribution, normal orientations, or spectral values, turned out to be the best choice for the classification in urban environments.

This paper presents an approach to exploit the richer information of sensor data provided by 3d laser rangefinders for the purpose of person tracking. Introduced is a method to adapt the observation model of a particle filter, to identify partial and full occlusions of a person, to determine the amount of occlusion behind an obstacle, and the occluding obstacle itself. This is done by tracing rays from positions near the person to the sensor and determining whether the ray hits an obstacle. The laser range data is represented using a voxel grid, which facilitates efficient retrieval and data reduction. As our experiments show, our proposed tracking approach is able to reliably keep track of a person in real-time, even when only partially visible, when moving in uneven terrain, or when the person passes closely another person of different size.

Segmenting range data into semantic categories has become a more and more active field of research in robotics. In this paper, we advocate to view this task as a problem of fast, large-scale retrieval. Intuitively, given a dataset of millions of labeled scan points and their neighborhoods, we simply search for similar points in the datasets and use the labels of the retrieved ones to predict the labels of a novel point using some local prediction model such as majority vote or logistic regression. However, actually carrying this out requires highly efficient ways of (1) storing millions of scan points in memory and (2) quickly finding similar scan points to a target scan point. In this paper, we propose to address both issues by employing Weiss et al.'s recent spectral hashing. It represents each item in a database by a compact binary code that is constructed so that similar items will have similar binary code words. In turn, similar neighbors have codes within a small Hamming distance of the code for the query. Then, we learn a logistic regression model locally over all points with the same binary code word. Our experiments on real world 3D scans show that the resulting approach, called spectrally hashed logistic regression, can be ultra fast at prediction time and outperforms state-of-the art approaches such as logistic regression and nearest neighbor.

In feature-based stereo analysis the use of restrictive knowledge-based assumptions constrains effectively the processes of interpreting and matching image features. Among these assumptions the general position assumption plays a significant role and is employed in many approaches. But working with complex scenes shows that this assumption is quite often violated for some parts of the scene. The paper proposes an active stereovision approach which completes automatically the reconstruction of a given scene and avoids thereby special views and errors in the interpretation and matching of image features.>