This paper deals with the problems of map building and mobile robot localization. Ultrasonic sensors information, obtained as the robot moves, is integrated in order to build a map of the environment. This map is afterwards used for robot localization, correcting the errors that the dead reckoning system accumulates in long displacements. The approach focuses on the way to analyze sensor inform...

It is argued that the following three properties are foundations of robust robot navigation: The use of landmarks (and, in particular, the use of a compass sense), the use of canonical paths, and the use of topological rather than geometrical maps. Some examples of successful animal navigation are presented that support this view. We have performed initial experiments with mobile robots to inve...

Evidence grids provide a uniform representation fo r fusing temporally and spatially distinct sensor readings. However, the use of evidence grids requires that the robot be localizeo! within its environment. Odometry errors typically accumulate over time, making localization estimates degrade, and introducing significant errors into evidence grads as they are built. W e have addressed this prob...

Abstract Background Fine-scale data on animal position are increasingly enabling us to understand the details of movement ecology and dead-reckoning, a technique integrating motion sensor-derived information heading speed, can be used reconstruct fine-scale paths at sub-second resolution, irrespective environment. On its own however, dead-reckoning process is prone cumulative errors, so that es...

and perspective-independent. So the first challenge for the imagistic model is to account for capacities like allocentric spatial navigation, which seems to draw on a different set of representations than those used in perception. This is not a matter of simply adding dimensions to the same perceptual space, but of mapping locations in a different kind of space. Putting the point a little diffe...