@INPROCEEDINGS{8324799, 
author={S. Wang and H. Huang and M. Liu}, 
booktitle={2017 IEEE International Conference on Robotics and Biomimetics (ROBIO)}, 
title={Simultaneous clustering classification and tracking on point clouds using Bayesian filter}, 
year={2017}, 
volume={}, 
number={}, 
pages={2521-2526}, 
abstract={Simultaneous Clustering, Classification and Tracking (SCCT) maintains many challenges, especially for point cloud data. SCCT is an essential process to facilitate the autonomous mobile systems. We present a novel unified framework from the object extraction to tracking with real-time performance. The framework can be described as five sub-tasks: ground extraction, clustering, recognition, tracking and representation. We compare the adjacent two frames to solve dense tracking and motion estimation. The state of each clustered object (moving or static) is estimated by using Spatial-Temporal methods. The distinguish objects with different features are extracted. Conditional Random Field and Bayesian filter are adopted to solve the data association problem. All the algorithmic modules have been tested on both outdoor actual environments and indoor simulation situations. The results indicate the efficiency and effectiveness of the proposed method.}, 
keywords={Bayes methods;feature extraction;filtering theory;image filtering;image fusion;image motion analysis;mobile robots;motion estimation;object detection;pattern clustering;target tracking;Bayesian filter;SCCT;autonomous mobile systems;clustered object;clustering task;conditional random field;data association problem;dense tracking;ground extraction;motion estimation;object extraction;point cloud data;recognition task;representation task;simultaneous clustering classification and tracking;spatial-temporal methods;tracking task;unified framework;Bayes methods;Feature extraction;Image color analysis;Laser radar;Probability distribution;Sensors;Three-dimensional displays}, 
doi={10.1109/ROBIO.2017.8324799}, 
ISSN={}, 
month={Dec},}