Multi-Variable State Prediction: HMM Based Approach for Real-Time Trajectory Prediction
Ankit, Karthik Narayanan, Dibyendu Ghosh, Vinayak Honkote, Ganeshram Nandakumar
Abstract
Predicting the motion of observed entities benefits humans almost seamlessly. The same benefits can be proliferated to mobile autonomous systems if we have a reliable, real-time solution to predict the motion of any object of interest, be it the host’s own motion or that of an observed foreign object. In this work, a novel Multi-Variable State Prediction (MVSP) methodology is devised for real-time trajectory prediction. MVSP incorporates cascaded stages of HMM with Viterbi algorithm and probabilistic quantization for accurately predicting the motion characteristics of the moving object. The overall scheme is employed to predict the motion of moving objects in a 3D space. The proposed approach is verified on both synthetically generated data sequences and data-sets captured from real-life experiments. For a practical scenario, the experiments resulted in an RMS error of 0.6m for a predicted distance of ~18m demonstrating the effectiveness and accuracy of the proposed methodology.
BibTeX
@inproceedings{iros2021_multivariablesta,
title = {Multi-Variable State Prediction: HMM Based Approach for Real-Time Trajectory Prediction},
author = {Ankit and Karthik Narayanan and Dibyendu Ghosh and Vinayak Honkote and Ganeshram Nandakumar},
booktitle = {IROS 2021},
year = {2021}
}