FISST-SLAM: Finite Set Statistical Approach to Simultaneous Localization and Mapping

The solution to the problem of mapping an environment and at the same time using this map to localize (the simultaneous localization and mapping, SLAM, problem) is a key prerequisite in the synthesis of truly autonomous vehicles. By far the most common formulation of the SLAM problem is founded on a vector based stochastic framework, where the sensor models and the vehicle models are represented in state-space form and the joint posterior or its statistics are obtained based on recursive Bayesian estimation. All of these SLAM solutions leading from the stochastic vector state-space approach require that we solve certain parallel problems in each recursion. These include effective solutions to the problems of data association, feature extraction, clutter filtering, and landmark or map management. In this paper, we propose an alternative framework based on finite set statistics (FISST), where the SLAM problem is reformulated so that the landmark map and the measurements are represented ...

• Type: Journal
• Year: 2010
• Where: IJRR
• Authors: Bharath Kalyan, K. W. Lee, W. Sardha Wijesoma
• Keywords: IJRR 2010, Posterior, Vehicle, Vehicle Trajectory