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For many indoor robot applications it is crucial to recognize doors reliably. Doors connect rooms and hallways and therefore determine possible navigation paths. Moreover, doors are important landmarks for robot self-localization and map building. Existing algorithms for door detection are often limited to restricted environments. They do not consider the large intra-class variability of doors. In this paper we present an approach which combines a set of simple door detection classifiers. The classifiers are based either on visual information or on laser ranger data. Separately, these classifiers accomplish only a weak door detection rate. However, by combining them with the aid of AdaBoost Algorithm more than 82% of all doors with a false positive rate less than 3% are detected in static test data. Further improvement can easily be achieved by using different door perspectives from a moving robot. In a realtime mobile robot application we detect more than 90% of all doors with a very low false detection rate.

Autonomous navigation in vegetated terrain requires the ability to discriminate obstacles from grass, a non-trivial problem when the sensorial world of the robot is based only on range information as provided, for example, by a laser rangefinder (ladar). We present a statistical analysis of the range data produced by a single-axis ladar in different situations, including the case of an obstacle partially occluded by grass. Such analysis inspired a simple classification algorithm, which has been tested on real range data acquired by JPL’s urban robot.

We developed an improved method of distance measurement by laser rangefinder — the method of sampling. Its favourable characteristics are the increased reliability of ranging (reduced false alarm rate), increased number of ranged targets, lower consumption and costs, and exclusive use of standard and easily obtainable components.

This paper describes a representation and computational model for deriving three dimensional, articulated volumetric descriptions of objects from laser rangefinder data. What differentiates this work from other approaches is that it is purely bottom-up, relying on general assumptions cast in terms of differential geometry.

The progress in microelectronics, laser technology, new materials and computer aided design of mechanical and optical subassemblies in the last 8 years has brought about a 50% diminution of the typical hand-held laser rangefinder size and 60% of its volume. There are several miniature rangefinders of 1 to 1.5 kg on the market, but they are merely ranging devices. None of them represents a complete rangefinding and observation device, least of all in binocular form.