D. Bui, M.D. Levine The main tasks of an autonomous mobile robot are path planning and navigation. A map of the environment is necessary for these tasks, and there are several ways one can be produced. It can either be explicitly predefined to the robot, or in an unknown environment, be built dynamically. We are interested in entering uncharted areas, so it is imperative that the robot be able to build an accurate map of its immediate surroundings in order to plan its next step. This research deals with a new sensor platform, QUADRIS, which is ideal for these tasks as well as for object recognition. QUADRIS is a range sensing system comprised of two independently controlled laser rangefinders, which have two degrees of freedom each, a pan and a tilt movement. The laser rangefinders can be used together in a stereo mode, or independently in a ``divergent stereo'' mode, by pointing them in opposite directions. Having this flexibilty will facilitate covering as much of the robot's surroundings as possible during the map building. The QUADRIS platform is mounted on a mobile robot, which is sent into the environment to be scanned to retrieve range data, which can then be used for various purposes. The environment we are working with is an office space. The tasks are mobile robot path planning, navigation, and landmark recognition of structures and objects. The first phase is the path planning, which is done using the ``Task Driven Behavioral Navigation (TBDN)'' system developed at McGill. In navigating through the hallways of an office environment, QUADRIS provides the range data for the TDBN system, which uses its behavioral architecture to perform dynamic path planning. The second phase is the recognition of landmarks that are generally found in an office space. These landmarks can be categorized into the following groups: (i) structural objects, such as doors and walls (ii) movable objects, which basically constitutes furniture such as desks and chairs (iii) parametric geons, such as blocks and cylinders, which are specific shapes placed in the environment. The recognition process entails constructing a ``viewing sphere'' object model, using relevant features extracted from 3-D models of the objects listed above. A viewing sphere is an imaginary sphere centered about the geometrical model of an object, and represents all the different possible views of that object. The surface of the sphere is divided into regions from which the object appears to share the same features. The distinct qualitative structure of the view from each of these regions is called an ``aspect''. Aspects may be defined in any number of ways. To define our aspects we will use features obtainable from QUADRIS, namely the presence of geometric planes on the object, its height and its material construction. The recognition process is then a comparison of a view of an unknown object with the data stored in the viewing sphere model. Using QUADRIS this system is capable of mobile robot navigation as well as object recognition. In navigating through the hallways of an office environment, QUADRIS provides the range data for the path planning, as well as for the recognition of the structural landmarks outlined above. Practical applications for this system include different security needs, such as office and warehouse surveillance.