Trajectory Prediction for Robot Navigation using Flow-Guided Markov Neural Operator

TL;DR

This paper introduces FlowMNO, a flow-guided neural operator that models pedestrian trajectories as a Markov process, significantly improving prediction accuracy for robot navigation in crowded environments.

Contribution

The novel FlowMNO model integrates optical flow with a Markov neural operator to predict pedestrian paths, outperforming existing deep learning methods.

Findings

FlowMNO outperforms state-of-the-art methods by approximately 86.46%.

Experiments conducted on multiple benchmark datasets validate the model's effectiveness.

FlowMNO enhances robot navigation safety and efficiency in crowded scenarios.

Abstract

Predicting pedestrian movements remains a complex and persistent challenge in robot navigation research. We must evaluate several factors to achieve accurate predictions, such as pedestrian interactions, the environment, crowd density, and social and cultural norms. Accurate prediction of pedestrian paths is vital for ensuring safe human-robot interaction, especially in robot navigation. Furthermore, this research has potential applications in autonomous vehicles, pedestrian tracking, and human-robot collaboration. Therefore, in this paper, we introduce FlowMNO, an Optical Flow-Integrated Markov Neural Operator designed to capture pedestrian behavior across diverse scenarios. Our paper models trajectory prediction as a Markovian process, where future pedestrian coordinates depend solely on the current state. This problem formulation eliminates the need to store previous states. We conducted experiments using standard benchmark datasets like ETH, HOTEL, ZARA1, ZARA2, UCY, and RGB-D pedestrian datasets. Our study demonstrates that FlowMNO outperforms some of the state-of-the-art deep learning methods like LSTM, GAN, and CNN-based approaches, by approximately 86.46% when predicting pedestrian trajectories. Thus, we show that FlowMNO can seamlessly integrate into robot navigation systems, enhancing their ability to navigate crowded areas smoothly.