The simultaneous localization and map building (SLAM) problem asks if it is possible for an autonomous vehicle to start in an unknown location in an unknown environment and then to incrementally build a map of this environment while simultaneously using this map to compute absolute vehicle location. Starting from estimation-theoretic foundations of this problem, the paper proves that a solution to the SLAM problem is indeed possible. The underlying structure of the SLAM problem is first elucidated. A proof that the estimated map converges monotonically to a relative map with zero uncertainty is then developed. It is then shown that the absolute accuracy of the map and the vehicle location reach a lower bound defined only by the initial vehicle uncertainty. Together, these results show that it is possible for an autonomous vehicle to start in an unknown location in an unknown environment and, using relative observations only, incrementally build a perfect map of the world and to compute simultaneously a bounded estimate of vehicle location. The paper also describes a substantial implementation of the SLAM algorithm on a vehicle operating in an outdoor environment using millimeter-wave radar to provide relative map observations. This implementation is used to demonstrate how some key issues such as map management and data association can be handled in a practical environment. The results obtained are cross-compared with absolute locations of the map landmarks obtained by surveying. In conclusion, the paper discusses a number of key issues raised by the solution to the SLAM problem including suboptimal map-building algorithms and map management.

Discusses the use of a 77 GHz millimetre wave radar as a guidance sensor for autonomous land vehicle navigation. An extended Kalman filter (EKF) maintains an estimate of map features in addition to vehicle state. This map augmented EKF is implemented with radar reflectors, designed for high visibility from all orientations. An improvement on this approach enables natural features to be identified, using the polarisation of returned radar signals. Those that are suitable as navigational markers are used to update estimates of vehicle and feature locations. This method requires no a priori knowledge of the environment and no target infrastructure.

This paper discusses the use of a 77 GHz millimeter wave radar as a guidance sensor for autonomous land vehicle navigation. A test vehicle has been fitted with a radar and encoders that give steer angle and velocity. An extended Kalman filter optimally fuses the radar range and bearing measurements with vehicle control signals to give estimated position and variance as the vehicle moves around a test site. The effectiveness of this data fusion is compared with encoders alone and with a satellite positioning system. Consecutive scans have been combined to give a radar image of the surrounding environment. Data in this format are invaluable for future work on collision detection and map building navigation.