A New Multimodal Map Building Method Using Multiple Object Tracking and Gaussian Process Regression

Recent advancements in simultaneous localization and mapping (SLAM) have significantly improved the handling of dynamic objects. Traditionally, SLAM systems mitigate the impact of dynamic objects by extracting, matching, and tracking features. However, in real-world scenarios, dynamic object information critically influences decision-making processes in autonomous navigation. To address this, we present a novel approach for incorporating dynamic object information into map representations, providing valuable insights for understanding movement context and estimating collision risks. Our method leverages on-site mobile robots and multiple object tracking (MOT) to gather activation levels. We propose a multimodal map framework that integrates occupancy maps obtained through SLAM with Gaussian process (GP) modeling to quantify the activation levels of dynamic objects. The Gaussian process method utilizes a map-based grid cell algorithm that distinguishes regions with varying activation levels while providing confidence measures. To validate the practical effectiveness of our approach, we also propose a method to calculate additional costs from the generated maps for global path planning. This results in path generation through less congested areas, enabling more informative navigation compared to traditional methods. Our approach is validated using a diverse dataset collected from crowded environments such as a library and public square and is demonstrated to be intuitive and to accurately provide activation levels.