Parameter Optimization for Loop Closure Detection in Closed Environments
Image by Nils Rottmann
Abstract
Tuning parameters is crucial for the performance of localization and mapping algorithms. In general, the tuning of the parameters requires expert knowledge and is sensitive to information about the structure of the environment. In order to design truly autonomous systems the robot has to learn the parameters automatically. Therefore, we propose a parameter optimization approach for loop closure detection in closed environments which requires neither any prior information, e.g. robot model parameters, nor expert knowledge. It relies on several path traversals along the boundary line of the closed environment. We demonstrate the performance of our method in challenging real world scenarios with limited sensing capabilities. These scenarios are exemplary for a wide range of practical applications including lawn mowers and household robots.
Video of the presentation, Corresponding publication
Nils Rottmann
Team Lead for Robotics & Autonomous Systems
With September 2021, Nils Rottmann started as a Software Developer/Product Owner at the Hako GmbH. He studied Theoretical Mechanical Engineering at the Hamburg University of Technology, Germany and holds a PhD in Robotics from the University of Luebeck, Germany, In his PhD with the title “Smart Sensor, Navigation and Learning Strategies for low-cost lawn care Systems”, he developed low-cost sensor systems and investigated probabilistic learning and modeling approaches. Currenlty, he works as a Team Lead for the robotic section at the Hako GmbH.