Human detection and tracking in an assistive living service robot through multimodal data fusion
A new method is proposed for using a combination of measurements from a laser range finder and a depth camera in a data fusion process that benefits from each modality’s strong side. The combination leads to a significantly improved performance of the human detection and tracking in comparison with what is achievable from the singular modalities. The useful information from both laser and depth camera is automatically extracted and combined in a Bayesian formulation that is estimated using a Markov Chain Monte Carlo (MCMC) sampling framework. The experiments show that this algorithm can track robustly multiple people in real world assistive robotics applications.
human detection human tracking service robotics assistive technology MCMC sensor data fusion
Alexandre Noyvirt Renxi Qiu
School of EngineeringCardiff UniversityUK School of Engineering Cardiff University UK
国际会议
IEEE 10th International Conference on Industrial Informatics(第十届IEEE工业信息学国际学术会议 INDIN2012)
北京
英文
1176-1181
2012-07-25（万方平台首次上网日期，不代表论文的发表时间）