In this paper we address the joint problems of automated data acquisition and view planning for large-scale indoor and outdoor sites. Our method proceeds in two distinct stages. In the initial stage, the system is given a 2-D map with which it plans a minimal set of sufficient covering views. We then use a 3-D laser scanner to take scans at each of these views. When this planning system is combined with our mobile robot, it automatically computes and executes a tour of these viewing locations and acquires the views with the robot's onboard laser scanner. These initial scans serve as an approximate 3-D model of the site. The planning software then enters a second stage in which it updates this model by using a voxel-based occupancy procedure to plan the next best view. This next best view is acquired, and further next best views are sequentially computed and acquired until a complete 3-D model is obtained. Results are shown for Fort Jay on Governors Island in the City of New York and for the church of Saint Menoux in the Bourbonnais region of France.

We present a systematic method for constructing 3-D models of large outdoor sites. The method is designed for a mobile robot platform and incorporates automated acquisition of scanned data as well as automated view planning and model construction. In our modeling process, we first use a preliminary view or set of preplanned views to yield an initial, approximate, 3-D model of the target structure. Then, we update this model by using a voxel-based procedure to plan and acquire the next best view. This updating is repeated sequentially until an accurate and complete 3-D model is finally obtained. The method was successfully tested on a portion of the Columbia University campus

We have demonstrated that a properly-equipped mobile robot can easily construct a detailed map of the wireless coverage of an urban environment. The Autonomous Vehicle for Exploration and Navigation of Urban Environments (AVENUE) mobile robot was successfully used to generate such maps in both manual and autonomous modes of operation. The resulting database contained a wealth of information for many different positions in the region, with a list of all access points viewable from each location together with a quality measure (the signal-to-noise ratio) of every detected signal. At a later time, the AVENUE system effectively used the data in this map to determine the approximate position of the robot as it traveled through the urban area.

We present a system for topologically localizing a mobile robot using color histogram matching of omnidirectional images. The system is intended for use as a navigational tool for the autonomous vehicle for exploration and navigation of urban environments (AVENUE) mobile robot. Our method makes use of omnidirectional images which are acquired from the robot's on-board camera. The method is fast and rotation invariant. Our tests have indicated that normalized color histograms are best for an outdoor environment while normalization is not required for indoor work. The system quickly narrows down the robot's location to one or two regions within the much larger test environment. Using this regional localization information, other vision systems that we have developed can further localize the robot.

A distributed, modular, heterogeneous architecture is presented that illustrates an approach to solving and integrating common tasks in mobile robotics, such as path planning, localization, sensor fusion, environmental modeling, and motion control. Experimental results are shown for an autonomous navigation task to confirm the applicability of our approach.