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— This paper describes two robotic systems developed for acquiring accurate volumetric maps of underground mines. One system is based on a cart instrumented by laser range finders, pushed through a mine by people. Another is a remotely controlled mobile robot equipped with laser range finders. To build consistent maps of large mines with many cycles, we(More)
Indoor environments can typically be divided into places with different functionali-ties like corridors, rooms or doorways. The ability to learn such semantic categories from sensor data enables a mobile robot to extend the representation of the environment facilitating the interaction with humans. As an example, natural language terms like " corridor " or(More)
The problem of generating maps with mobile robots has received considerable attention over the past years. Most of the techniques developed so far have been designed for situations in which the environment is static during the mapping process. Dynamic objects, however, can lead to serious errors in the resulting maps such as spurious objects or(More)
— To operate outdoors or on non-flat surfaces, mobile robots need appropriate data structures that provide a compact representation of the environment and at the same time support important tasks such as path planning and localization. One such representation that has been frequently used in the past are elevation maps which store in each cell of a discrete(More)
Elevation maps are a popular data structure for representing the environment of a mobile robot operating outdoors or on not-flat surfaces. Elevation maps store in each cell of a discrete grid the height of the surface at the corresponding place in the environment. However, the use of this 2 1 2-dimensional representation, is disadvantageous when utilized(More)
— We present a novel approach for unsupervised discovery of repetitive objects from 3D point clouds. Our method assumes that objects are non-deformable and uses multiple occurrences of an object as the evidence for its existence. We segment input range data by superpixel segmentation and extract features for each segment. We search for a group of segments(More)
In this paper we consider the problem of mobile robot localization with range sensors in outdoor environments. Our approach applies a particle filter to estimate the full six-dimensional state of the robot. To represent the environment we utilize multi-level surface maps which allow the robot to represent vertical structures and multiple levels in the(More)
People tracking is a key technology for autonomous systems, intelligent cars and social robots operating in populated environments. What makes the task di cult is that the appearance of humans in range data can change drastically as a function of body pose, distance to the sensor, self-occlusion and oc-clusion by other objects. In this paper we propose a(More)
— In this paper we present an efficient technique to learn Associative Markov Networks (AMNs) for the segmentation of 3D scan data. Our technique is an extension of the work recently presented by Anguelov et al. [1], in which AMNs are applied and the learning is done using max-margin optimization. In this paper we show that by adaptively reducing the(More)
Recently, the problem of learning volumetric maps from three-dimensional range data has become quite popular in the context of mobile robotics. One of the key challenges in this context is to reduce the overall amount of data. The smaller the number of data points, however , the fewer information is available to register the scans and to compute a(More)