SLAM using Incremental Probabilistic PCA and Dimensionality Reduction

The recent progress in robot mapping (or SLAM) algorithms has focused on estimating either point features (such as landmarks) or grid-based representations. Both of these representations generally scale with the size of the environment, not the complexity of the environment. Many thousand parameters may be required even when the structure of the environment can be represented using a few geometric primitives with many fewer parameters. We describe a novel SLAM model called IPSLAM. Our algorithm clusters sensor data into line segments using the Probabilistic PCA algorithm, which provides a data likelihood model that can be used within a SLAM algorithm for the simultaneous estimation of map and robot pose parameters. Unlike previous work in extracting line-based representations from point-based maps, IPSLAM builds non-point-based maps directly from the sensor data. We demonstrate our algorithm on mapping part of the MIT Stata Centre.