Dynamic Hilbert Maps: Real-Time Occupancy Predictions in Changing Environment
This addresses the challenge of modeling temporal dependencies in occupancy predictions for applications like urban streets or crowded areas, but it appears incremental as it builds on static occupancy models.
The paper tackled the problem of learning and predicting occupancy levels in dynamic environments, achieving real-time performance with LIDAR data.
This paper addresses the problem of learning instantaneous occupancy levels of dynamic environments and predicting future occupancy levels. Due to the complexity of most real-world environments, such as urban streets or crowded areas, the efficient and robust incorporation of temporal dependencies into otherwise static occupancy models remains a challenge. We propose a method to capture the spatial uncertainty of moving objects and incorporate this uncertainty information into a continuous occupancy map represented in a rich high-dimensional feature space. Experiments performed using LIDAR data verified the real-time performance of the algorithm.