A Generic Approach to Self-localization and Mapping of Mobile Robots Without Using a Kinematic Model

In this paper a generic approach to the SLAM (Simultaneous Localization and Mapping) problem is proposed. The approach is based on a probabilistic SLAM algorithm and employs only two portable sensors, an inertial measurement unit (IMU) and a laser range finder (LRF) to estimate the state and environment of a robot. Scan-matching is applied to compensate for noisy IMU measurements. This approach does not require any robot-specific characteristics, e.g. wheel encoders or kinematic models. In principle, this minimal sensory setup can be mounted on different robot systems without major modifications to the underlying algorithms. The sensory setup with the probabilistic algorithm is tested in real-world experiments on two different kinds of robots: a simple two-wheeled robot and the six-legged hexapod AMOSII. The obtained results indicate a successful implementation of the approach and confirm its generic nature. On both robots, the SLAM problem can be solved with reasonable accuracy.