Multimodal Motion Prediction Based on Adaptive and Swarm Sampling Loss Functions for Reactive Mobile Robots

Making accurate predictions about the dynamic environment is crucial for the trajectory planning of mobile robots. Predictions are by nature uncertain, and for motion prediction multiple futures are possible for the same historic behavior. In this work, the objective is to predict possible future positions of the target object for the collision avoidance purpose for mobile robots by considering different uncertainty by combining a sampling-based idea with data-driven methods. More specifically, we propose a major improvement on a loss function for multiple hypotheses and test it with convolutional neural networks on motion prediction problems. We implement post-processing heuristics that produce multiple Gaussian distribution estimations, and show that the result is suitable for trajectory planning for mobile robots. The method is also evaluated with the Stanford Drone Dataset.