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Robot navigation in poorly structured and uneven outdoor environments is an unsolved problem. Thus we present a SLAM (simultaneous localization and mapping) approach that is based on “leveled range scans”. The method is combining 3D perception with 2D localization and mapping. In this way established path planning and 2D navigation algorithms can be used in(More)
Quite a number of approaches for solving the simultaneous localization and mapping (SLAM) problem exist by now. Some of them have recently been extended to mapping environments with six-degree-of-freedom poses, yielding 6D SLAM approaches. To demonstrate the capabilities of the respective algorithms, it is common practice to present generated maps and(More)
This paper presents a combination of a 3D laser sensor and a line-base SLAM algorithm which together produce 2D line maps of highly cluttered indoor environments. The key of the described method is the replacement of commonly used 2D laser range sensors by 3D perception. A straightforward algorithm extracts a virtual 2D scan that also contains partially(More)
A basic task of rescue robot systems is mapping of the environment. Localizing injured persons, guiding rescue workers and excavation equipment requires a precise 3D map of the environment. This paper presents a new 3D laser range finder and novel scan matching method for the robot Kurt3D [9]. Compared to previous machinery [12], the apex angle is enlarged(More)
This paper presents two laser scanner based approaches to locate and pick-up pallets with the aim of automating forklift trucks. In contrast to camera based systems our approaches are independent of luminance conditions which can be highly variable in industrial environments. Whereas one approach uses pallets modified with adhesive reflectors which enables(More)
This paper introduces a new perceptual model for Monte Carlo localization (MCL). In our approach a 3D laser scanner is used to observe the ceiling. The MCL matches ceiling structures like beams, columns, air condition and lightning installation against a world model containing line and point features. Thus the localization is not effected by clutter or any(More)
In the past many solutions for simultaneous localization and mapping (SLAM) have been presented. Recently these solutions have been extended to map large environments with six degrees of freedom (DoF) poses. To demonstrate the capabilities of these SLAM algorithms it is common practice to present the generated maps and successful loop closing. Unfortunately(More)
This paper introduces a localization based on GPS and laser measurements for urban and non-urban outdoor environments. In this approach, the GPS pose is Kalman filtered using wheel odometry and inertial data and tightly integrated into a Monte Carlo localization based on 3D laser range data and a line feature reference map. By applying to this kind of(More)
This paper presents a navigation system for mobile service robots working in urban environments. The system combines a 3D laser sensor with 2D algorithms for path planning and simultaneous localization and mapping (SLAM). In contrast to other map representations the colored 2D map, first presented in this paper, is able to hold information adapted to both(More)