Abstract. We present an efficient multi-resolution approach to segment a 3D point cloud into planar components. In order to gain efficiency, we process large point clouds iteratively from coarse to fine 3D resolutions: At each resolution, we rapidly extract surface normals to describe surface elements (surfels). We group surfels that cannot be associated with planes from coarser resolutions into coplanar clusters with the Hough transform. We then extract connected components on these clusters and determine a best plane fit through RANSAC. Finally, we merge plane segments and refine the segmentation on the finest resolution. In experiments, we demonstrate the efficiency and quality of our method and compare it to other state-of-the-art approaches.

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In recent years multiple simultaneous localization and mapping (SLAM) algorithms have been proposed, which address the challenges of 3D environments in combination with six degress of freedom in the robot position. Commonly, solutions based on scan-matching algorithms are applied. In contrast to these approaches, we propose to use a particle filter transferring the concept of the 2D Rao-Blackwellized particle filter SLAM to 3D. As filter input, 3D laser range data and odometry readings are obtained while the robot is in motion. The ground plane is estimated based on previously built map parts, thereby approaching the problem that not all degrees of freedom are covered by the odometry. To gain control of the high memory requirements for the particles' 3D map representations, we introduce a memory efficient search structure and adapt a technique to efficiently organize and share maps between particles. We evaluate our approach based on experimental results obtained by sim ulation as well as measurements of a real robot system

This paper presents a prototype of a multi-robot reconnaissance system for detection of chemical, biological, radiological, nuclear, and explosive (CBRNE) threats. Different robot platforms are able to carry highly modularized payload platforms: either a multi-purpose CBRNE sensor suite or a mobile manipulator for semi-autonomous sample collection. A variety of sensors is available for detection and identification of chemical and radiological hazards as well as for collecting potentially dangerous airborne biological particles. The manipulator is used to gather chemical and biological samples directly from surfaces in the environment. Unlike in most industrial applications the environment is not known a priori, therefore a randomized path planning algorithm is used to generate collision-free trajectories. A detailed description illustrates the robot platforms, the CBRNE sensor suite, and the mobile manipulator as well as relevant parts of control software and user interface. For a technical validation preliminary real world experiments are presented.

Robots are primarily deployed for tasks which are dirty, dull, or dangerous. While the former two are already highly automated, many dangerous tasks such as explosive ordnance disposal or inspection in hazardous environments are predominantly done via tele-operation. Usually, such tasks require the manipulation of objects in a way that cannot be done reliably with automated systems. In this paper, we present a method to tele-operate the manipulator of a robot by transferring the operator's arm movement. The movement is recorded with inertial measurement units which can be sewn into clothing and need no external infrastructure like cameras or motion capture systems. The lack of intermediate user interfaces (e.g. joysticks) makes this control method very intuitive and easy to learn. We demonstrate this with two different NIST manipulation tests and as part of an integrated system for the ELROB robot competition