This article provides a comprehensive introduction into the field of robotic mapping, with a focus on indoor mapping. It describes and compares various probabilistic techniques, as they are presently being applied to a vast array of mobile robot mapping problems. The history of robotic mapping is also described, along with an extensive list of open research problems.

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 — This paper addresses the problems of building a functional mobile robot for urban site navigation and modeling with focus on keeping track of the robot location. We have developed a localization system that employs two methods. The first method uses odometry, a compass and tilt sensor, and a global positioning sensor. An extended Kalman filter integrates the sensor data and keeps track of the uncertainty associated with it. The second method is based on camera pose estimation. It is used when the uncertainty from the first method becomes very large. The pose estimation is done by matching linear features in the image with a simple and compact environmental model. We have demonstrated the functionality of the robot and the localization methods with real-world experiments. Index Terms — Mobile robots, localization, machine vision I.

This paper describes an algorithm for generating compact 3D models of indoor environments with mobile robots. Our algorithm employs the expectation maximization algorithm to fit a lowcomplexity planar model to 3D data collected by range finders and a panoramic camera. The complexity of the model is determined during model fitting, by incrementally adding and removing surfaces. In a final post-processing step, measurements are converted into polygons and projected onto the surface model where possible. Empirical results obtained with a mobile robot illustrate that high-resolution models can be acquired in reasonable time. 1.

This article presents GraphSLAM, a unifying algorithm for the offline SLAM problem. GraphSLAM is closely related to a recent sequence of research papers on applying optimization techniques to SLAM problems. It transforms the SLAM posterior into a graphical network, representing the log-likelihood of the data. It then reduces this graph using variable elimination techniques, arriving at a lowerdimensional problems that is then solved using conventional optimization techniques. As a result, GraphSLAM can generate maps with 10 8 or more features. The paper discusses a greedy algorithm for data association, and presents results for SLAM in urban environments with occasional GPS measurements.

This paper presents a real-time algorithm for acquiring compact 3D maps of indoor environments, using a mobile robot equipped with range and imaging sensors. Building on previous work on real-time pose estimation during mapping [1], our approach extends the popular expectation maximization algorithm [2] to multi-surface models, and makes it amenable to real-time execution. Maps acquired by our algorithm consist of compact sets of textured polygons that can be visualized interactively. Experimental results obtained in corridor-type environments illustrate that compact and accurate maps can be acquired in real-time and in a fully automated fashion.

Estimating geometric structure from uncalibrated images accurately enough for high quality rendering is difficult. We present a method where only coarse geometric structure is tracked and estimated from a moving camera. Instead a precise model of the intensity image variation is obtained by overlaying a dynamic, time varying texture on the structure. This captures small scale variations (e.g. non-planarity of the rendered surfaces, small camera geometry distortions and tracking errors). The dynamic texture is estimated and coded much like in movie compression, but parameterized in 6D pose instead of time, hence allowing the interpolation and extrapolation of new poses in the rendering and animation phase. We show experiments tracking and re-animating natural scenes as well as evaluating the geometric and image intensity accuracy on constructed special test scenes.

In this paper we consider the problem of three-dimensional scan registration for autonomous mobile vehicles. The problem of 3D scan matching is of enormous importance for the construction of metric representations of the environment, for localization, and for navigation planning in the three-dimensional space. We present a probabilistic technique that computes a probability density for each pair of scans and then performs the registration using a greedy hill-climbing search in the likelihood space. We compare our approach with the ICP-algorithm which currently is the most popular technique for matching 3D range scans. We present experiments illustrating that the estimation error of the ICP algorithm is significantly higher than the resulting error obtained with our probabilistic approach.

A long standing goal in image-based modeling and rendering is to capture a scene from camera images and construct a sufficient model to allow photo-realistic rendering of new views. With the confluence of computer graphics and vision, the combination of research on recovering geometric structure from uncalibrated cameras with modeling and rendering has yielded numerous new methods. Yet, many challenging issues remain to be addressed before a sufficiently general and robust system could be built to e.g. allow an average user to model their home and garden from camcorder video.

Mapping an environment with an imaging sensor becomes very challenging if the environment to be mapped is unknown and has to be explored. Exploration involves the planning of views so that the entire environment is covered. The majority of implemented mapping systems use a heuristic planning while theoretical approaches regard only the traveled distance as cost. However, practical range acquisition systems spend a considerable amount of time for acquisition. In this paper, we address the problem of minimizing the cost of looking around a corner, involving the time spent in traveling as well as the time spent for reconstruction. Such a local exploration can be used as a subroutine for global algorithms. We prove competitive ratios for two online algorithms. Then, we provide two representations of local exploration as a Markov Decision Process and apply a known policy iteration algorithm. Simulation results show that for some distributions the probabilistic approach outperforms deterministic strategies. I.