Utilizing high-dimensional features for real-time robotic applications: Reducing the curse of dimensionality for recursive Bayesian estimation

IEEE International Conference on Intelligent Robots and Systems

Volumn 2016-November, Issue , 2016, Pages 1230-1237

  Li, Jie ^a   Ozog, Paul ^a   Abernethy, Jacob ^a   Eustice, Ryan M ^a   Johnson Roberson, Matthew ^a  

^a UNIVERSITY OF MICHIGAN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; BENCHMARKING; LEARNING SYSTEMS; ROBOT APPLICATIONS; ROBOTICS; ROBOTS; UNCERTAINTY ANALYSIS; UNDERWATER ACOUSTICS; UNDERWATER IMAGING;

CURSE OF DIMENSIONALITY; HIGH DIMENSIONAL FEATURE; LINEAR RELATIONSHIPS; LOCALIZATION ACCURACY; REAL TIME PERFORMANCE; REAL-TIME APPLICATION; RECURSIVE BAYESIAN ESTIMATION; ROBOTIC APPLICATIONS;

INTELLIGENT ROBOTS;

EID: 85006384854     PISSN: 21530858     EISSN: 21530866     Source Type: Conference Proceeding    
DOI: 10.1109/IROS.2016.7759205     Document Type: Conference Paper

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