Textured polygon meshes are becoming more and more important for robotic applications. In this paper we present an approach to automatically extract textures from colored 3D point cloud data and apply them to a polygonal reconstruction of the scene. The extracted textures are analyzed for existing patters and reused if several instances appear. Emphasis of this work is on minimizing the number of used pixels while maintaining a realistic impression of the scanned environment.

Three dimensional point clouds acquired by range scanners often do not represent the environment precisely due to noise and errors in the acquisition process. These latter systematical errors manifest as deformations of different kinds in the 3D range image. This paper presents a novel approach to correct deformations by an analysis of the structures present in the environment and correcting them by non-rigid transformations. The resulting algorithms are used for creating high-accuracy 3D indoor maps.

In order to construct three-dimensional shape models of large-scale architectural structures using a laser range finder, a number of range images are taken from various viewpoints. These images are aligned using post-processing procedures such as the ICP algorithm. However, in general, before applying the ICP algorithm, these range images must be aligned roughly by a human operator in order to converge to precise positions. The present paper proposes a new modeling system using a group of multiple robots and an on-board laser range finder. Each measurement position is identified by a highly precise positioning technique called Cooperative Positioning System (CPS), which utilizes the characteristics of the multiple-robot system. Thus, the proposed system can construct 3D shapes of large-scale architectural structures without any post-processing procedure or manual registration. ICP is applied optionally for a subsequent refinement of the model. Measurement experiments in unknown and large indoor/outdoor environments are carried out successfully using the newly developed measurement system consisting of three mobile robots named CPS-V. Generating a model of Dazaifu Tenmangu, a famous cultural heritage, for its digital archive completes the paper.

The great majority of path following control laws for either kinematical or dynamical mobile robot models are designed assuming ideal actuators, i.e. assuming that any commanded velocity or torque (in the kinematical and dynamical cases respectively) will be instantly implemented regardless of its value. Real actuators are far from being ideal. In particular, only bounded velocities and torques can be realized for any given command. With reference to the kinematical model of a differential drive mobile robot, a known path following control law is modified to account for actuator velocity saturation. The proposed solution is experimentally shown to be particularly useful for high speed applications where accounting for actuator velocity saturation may have a large influence on performance.

To create with an autonomous mobile robot a 3D volumetric map of a scene it is necessary to gage several 3D scans and to merge them into one consistent 3D model. This paper provides a new solution to the simultaneous localization and mapping (SLAM) problem with six degrees of freedom. Robot motion on natural surfaces has to cope with yaw, pitch and roll angles, turning pose estimation into a problem in six mathematical dimensions. A fast variant of the Iterative Closest Points algorithm registers the 3D scans in a common coordinate system and relocalizes the robot. Finally, consistent 3D maps are generated using a global relaxation. The algorithms have been tested with 3D scans taken in the Mathies mine, Pittsburgh, PA. Abandoned mines pose significant problems to society, yet a large fraction of them lack accurate 3D maps.

This paper describes a novel, laser-based approach for tracking the pose of a high-speed mobile robot. The algorithm is outstanding in terms of accuracy and computational time, being 33 times faster than real time. The efficiency is achieved by a closed form solution for the matching of two lasers scans, the use of natural landmarks and fast linear filters. The implemented algorithm is evaluated with the high-speed robot Kurt3D (4 m/s), and compared to standard scan matching methods in indoor and outdoor environments.