This paper presents an algorithm for full 3D shape reconstruction of indoor and outdoor environments with mobile robots. Data is acquired by a fastmoving robot equipped with two 2D laser range finders. Our approach combines an efficient scan matching routine for robot pose estimation with an algorithm for approximating environments using flat surfaces. On top of that, our approach includes a mesh simplification technique to reduce the complexity of the resulting models. In extensive experiments, our method is shown to produce accurate models of indoor and outdoor environments that compare favorably to other methods.

Abstract — We describe a probabilistic framework for detection and modeling of doors from sensor data acquired in corridor environments with mobile robots. The framework captures shape, color, and motion properties of door and wall objects. The probabilistic model is optimized with a version of the expectation maximization algorithm, which segments the environment into door and wall objects and learns their properties. The framework allows the robot to generalize the properties of detected object instances to new object instances. We demonstrate the algorithm on real-world data acquired by a Pioneer robot equipped with a laser range finder and an omni-directional camera. Our results show that our algorithm reliably segments the environment into walls and doors, finding both doors that move and doors that do not move. We show that our approach achieves better results than models that only capture behavior, or only capture appearance. I.

SLAT, the problem of tracking a target in a sensor network while simultaneously localizing and calibrating the nodes of the network. Our proposed solution, LaSLAT, is a Bayesian filter that provides on-line probabilistic estimates of sensor locations and target tracks. It does not require globally accessible beacon signals or accurate ranging between the nodes. Real hardware experiments are presented for 2D and 3D, indoor and outdoor, and ultrasound and audible ranging-hardware-based deployments. Results demonstrate rapid convergence and high positioning accuracy.

This article investigates the problem of acquiring 3D object maps of indoor household environments, in particular kitchens. The objects modeled in these maps include cupboards, tables, drawers and shelves, which are of particular importance for a household robotic assistant. Our mapping approach is based on PCD (point cloud data) representations. Sophisticated interpretation methods operating on these representations eliminate noise and resample the data without deleting the important details, and interpret the improved point clouds in terms of rectangular planes and 3D geometric shapes. We detail the steps of our mapping approach and explain the key techniques that make it work. The novel techniques include statistical analysis, persistent histogram features estimation that allows for a consistent registration, resampling with additional robust fitting techniques, and segmentation of the environment into meaningful regions. Key words: environment object model, point cloud data, geometrical reasoning 1

This paper describes an algorithm for generating compact feature-based 3D models of indoor environments with a mobile robot. The emphasis lies on the high performance of the algorithm, its possible incremental use, as well as its wide applicability to a variety of sensors as it does not assume any structure in the raw data at all. It recovers planar surfaces of physical environments based on a set of unorganized points {v1,..., vN}∈R 3 and generates a compact, real-time renderable 3D model. 1

Abstract — This paper presents a method for probabilistic plane fitting and an application to robotic 3D mapping. The plane is fitted in an orthogonal least-square sense and the output complies with the conventions of the Symmetries and Perturbation model (SPmodel). In the second part of the paper, the presented plane fitting method is used within a 3D mapping application. It is shown that by using probabilistic information, high precision 3D maps can be generated. I.

Abstract: The representation of the environment of a mobile robot by line models is a popular alternative to occupancy grid maps. Line maps require significantly less memory than occupancy grids and therefore scale better with the size of the environment. They furthermore are more accurate since they do not suffer from discretization problems. In the past a variety of techniques for learning line maps from range data have been developed. These techniques differ in various aspects such as the way lines are extracted from range scans, how the lines are updated upon sensory input. There furthermore are techniques that are able to operate online, whereas others postprocess the data. In this paper we compare three different techniques for learning line models with respect to various parameters such as efficiency and quality of the resulting maps. Experimental results illustrate the advantages and the disadvantages of the different techniques.

Precise digital 3D models of indoor environments are needed in several applications, e.g., facility management, architecture, rescue and inspection robotics. This paper presents a new algorithm that transforms a 3D volumetric model into a very precise compact 3D map and generates semantic descriptions. Our system is composed of a robust, autonomous mobile robot for the automatic data acquisition and a precise, cost effective, high quality 3D laser scanner to gage indoor environments. The reconstruction method consists of reliable scan matching and feature detection algorithms. The 3D scene is matched against a coarse semantic description of general indoor environments and the generated knowledge is used to refine the 3D model.

Abstract — Reliable mapping and self-localization in three dimensions while moving is essential to survey inaccessible work spaces or to inspect technical plants autonomously. Our solution to this 3D SLAM problem is novel in several respects. First, a new rotating laser-scanning setup is presented for acquiring point clouds and reducing them to surface patches in real time. Second, the SLAM algorithms work entirely on highly reduced, attributed surface models and in 3D. Third, we propose a novel system architecture of an Extended Kalman filter (EKF) for 3D position tracking, cooperating with a 3D range image understanding system for matching, aligning, and integrating overlapping range views. The system is demonstrated by an indoor exploration tour.

Precise digital 3D models of indoor environments are needed in several applications, e.g., facility management, architecture, rescue and inspection robotics. This paper presents a new method that transforms a 3D volumetric model, acquired by a mobile robot equipped with a 3D laser scanner, into a very precise compact 3D map and generates semantic descriptions. The scanned 3D scene is matched against a coarse semantic description of general indoor environments. The matching is done by a Prolog program compiled from the scanned 3D scene and combined with clauses from the coarse semantic description. The generated scene specific knowledge produced by the unification in the Prolog program is used to refine the 3D model.