Real-Thme 3-D Pose Estimation Using a High-Speed Range Sensor

This paper describes a system which can perform full 3-D pose estimation of a single arbitrarily shaped, rigid object at rates up to 10 Hz. A triangular mesh model of the object to be tracked is generated offline using conventional range sensors. Real-time range data of the object is sensed by the CMU high speed VLSI range sensor. Pose estimation is performed by registering the real-time range data to the triangular mesh model using an enhanced implementation of the Iterative Closest Point (ICP) Algorithm introduced by Besl and McKay (1992). The method does not require explicit feature extraction or specification of correspondence. Pose estimation accuracies of the order of 1% of the object size in translation, and 1 degree in rotation have been measured. >

Authors

• David A. Simon
• Martial Hebert
• Takeo Kanade

Keywords

• Iterative algorithms
• Iterative closest point algorithm
• Shape
• Feature extraction
• Cameras
• Motion estimation
• Data mining
• Sensor phenomena and characterization
• Sensor systems
• Robot sensing systems
• Pose Estimation
• Range Of Sensors
• Human Pose Estimation
• High Speed
• Rigid Body
• Closest Point
• Triangular Mesh
• Iterative Closest Point
• Triangular Model
• Scaling Factor
• Physical Body
• Cycle Time
• Human-computer Interaction
• Kalman Filter
• Optical Axis
• Noisy Data
• Object Motion
• Tracking Algorithm
• Object Pose
• Rotated Component
• Microrobots
• Registration Problem
• Alignment Condition
• CAD Model
• Binary Tree
• Sensor Resolution
• Real-time Estimation
• Object Surface