TY - JOUR
T1 - Probabilistic models for robot-based object segmentation
AU - Beale, Daniel
AU - Iravani, Pejman
AU - Hall, Peter
PY - 2011/12
Y1 - 2011/12
N2 - This paper introduces a novel probabilistic method for robot based object segmentation. The method integrates knowledge of the robot's motion to determine the shape and location of objects. This allows a robot with no prior knowledge of its workspace to isolate objects against their surroundings by moving them and observing their visual feedback. The main contribution of the paper is to improve upon current methods by allowing object segmentation in changing environments and moving backgrounds. The approach allows optimal values for the algorithm parameters to be estimated. Empirical studies against alternatives demonstrate clear improvements in both planar and three dimensional motion.
AB - This paper introduces a novel probabilistic method for robot based object segmentation. The method integrates knowledge of the robot's motion to determine the shape and location of objects. This allows a robot with no prior knowledge of its workspace to isolate objects against their surroundings by moving them and observing their visual feedback. The main contribution of the paper is to improve upon current methods by allowing object segmentation in changing environments and moving backgrounds. The approach allows optimal values for the algorithm parameters to be estimated. Empirical studies against alternatives demonstrate clear improvements in both planar and three dimensional motion.
UR - http://www.scopus.com/inward/record.url?scp=80053565312&partnerID=8YFLogxK
UR - http://dx.doi.org/10.1016/j.robot.2011.08.003
U2 - 10.1016/j.robot.2011.08.003
DO - 10.1016/j.robot.2011.08.003
M3 - Article
SN - 0921-8890
VL - 59
SP - 1080
EP - 1089
JO - Robotics and Autonomous Systems
JF - Robotics and Autonomous Systems
IS - 12
ER -