Automatic Tuning of RatSLAM’s Parameters by Irace and Iterative Closest Point

Simultaneous localization and mapping (SLAM) is a fundamental problem in mobile robotics. One solution to this problem is RatSLAM, which is a SLAM algorithm inspired by the navigation system in rodent brains. RatSLAM has a set of parameters that have to be adjusted individually for each environment to generate a suitable map. To date, there is no automatic tuning of these parameters and they are hand-tuned by trial-and-error. The present work proposes an automatic parameter tuning method, which employs the Iterative Closest Point (ICP) algorithm to evaluate the generated maps as well as the Irace algorithm to find the best parameters settings. The process is formulated as an optimization problem, where the objective function takes into account the parameters values, the generated RatSLAM's maps, and a ground truth map. Irace automatically generates a unique combination of RatSLAM parameter set, where each combination generates a map. Then, deviations between the generated and a ground truth map are evaluated with the ICP algorithm. These evaluations are taken into account by Irace to generate new combinations of RatSLAM parameters until this deviation reaches a stop criterion. The proposed methodology was successfully tested on three different scenarios. It was able to automatically estimate parameters to generate experience maps close to the ground truth maps. Furthermore, it showed how the tuned parameters can be shared across different scenarios, which might pave the way to develop similar approaches for other SLAM algorithms that also depend on fine-tuning of their parameters.