An approach to improve simultaneous localization and mapping in human populated environments

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One task that autonomous mobile robots have to perform in indoor spaces is to construct the map of their environment and report their location and orientation. This process is called Simultaneous Localization and Mapping (SLAM). To do so, robots extract data through their sensors. However, in dynamic indoor environments, moving objects induce the SLAM process to collapse or diverge. Moving objects should not be taken into account to generate the map and the occlusions that they generate should be solved. In this work, we propose a robust and flexible approach for SLAM algorithms to perform better in human populated environments; by integrating a filtering scheme that manages moving and static objects. To illustrate the suitability of our approach, we implement Gmapping, as the classical SLAM algorithm, and RANSAC as the filter. Nevertheless, any other SLAM algorithm and filter can be implemented. The simulation tests have been carried out using three museum environments, which the robot can face in real life. Through the results obtained, it is possible to conclude that the proposed approach is efficient in managing the sensor data, filtering the outliers, and thus removing dynamic objects from the map.