1/27/2024 0 Comments Occupancy grid mapping![]() Finally, we leverage the different map representations for accurate, robust localization with a combination of state-of-the-art algorithms. Then, an efficient technique converts these 2D OGMs into Pose Graph-based maps enabling more accurate robot pose tracking. These OGMs only represent structural elements allowing indoor autonomous robot navigation. First, 2D OGMs are automatically generated from complex BIM models. This paper proposes an open-source method to generate appropriate Pose Graph-based maps from BIM models for robust 2D-LiDAR localization in changing and dynamic environments. These deviations affect the accuracy of AMCL drastically. ![]() Discrepancies between the reference BIM model and the real world (Scan-BIM deviations) are not only due to furniture or clutter but also the usual as-planned and as-built deviations that exist with any model created in the design phase. ![]() However, most of these studies assume that the BIM model precisely represents the real world, which is rarely true. Download a PDF of the paper titled Occupancy Grid Map to Pose Graph-based Map: Robust BIM-based 2D-LiDAR Localization for Lifelong Indoor Navigation in Changing and Dynamic Environments, by Miguel Arturo Vega Torres and 2 other authors Download PDF Abstract:Several studies rely on the de facto standard Adaptive Monte Carlo Localization (AMCL) method to localize a robot in an Occupancy Grid Map (OGM) extracted from a building information model (BIM model). ![]()
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