FastSLAM filter implementation for indoor autonomous robot

In this paper, we present a FastSLAM particle filter algorithm used to efficiently map large indoor environments features. The proposed filter uses an unknown data association to match the extracted environment characteristics, such as walls and doors. Data association (DA) is chosen due to two reasons: 1) permit to rearrange the filter particles in the prediction phase of the filter, and 2) enable to incorporate the extracted features in the map of each particle. Indoor SLAM experiments were conducted in a long corridor composed by several wooden walls. These provisional walls were used to create a more challenging environment. From the map obtained by the mapping process, the robot is capable of navigating through the environment using the set of 22 predefined poses. The SLAM filter measurements are compared with their actual measured values.