Single Camera Object Tracking Using Inertial Sensor Fusion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current optical tracking systems require multiple cameras and markers to achieve full 6-degree-of-freedom tracking of objects, which can be cumbersome for slender objects with no convenient large flat surfaces, and often rely on environmental installations.
Innovation Solution
A method using a single camera and inertial sensors to compute the spatial location and orientation of an object by measuring two points on the object, with the camera's optical axis aligned with gravity, and correcting for drift using periodic updates from the camera image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple cameras and markers are used to achieve full 6-DOF tracking, then tracking accuracy is improved, but device complexity and hardware requirements increase
Solution Approach 1:
The patent combines multiple tracking functions into a single camera system by integrating inertial sensors with optical tracking. The inertial measurement unit (IMU) provides orientation data while the single camera captures position information, merging what would traditionally require multiple cameras into one integrated system that achieves 6-DOF tracking with reduced hardware complexity
Solution Approach 2:
The patent introduces inertial sensors as an intermediary component that bridges the gap between single-camera limitations and full 6-DOF tracking requirements. The inertial measurement unit provides complementary orientation data that compensates for the single camera's inability to independently determine all six degrees of freedom, enabling accurate tracking without requiring multiple cameras
2Measurement precision
If a triangular marker configuration is used to solve exterior orientation problem, then orientation accuracy is improved, but ease of operation deteriorates due to cumbersome mounting on slender objects
Solution Approach 1:
The patent segments the tracking system into two independent components: a minimal marker configuration (just two markers) for position tracking and an inertial measurement unit for orientation tracking. This segmentation allows the marker to be simple and easy to mount on slender objects while the IMU independently provides the orientation information that would traditionally require a complex triangular marker arrangement
Solution Approach 2:
The patent replaces the mechanical/geometric marker configuration (triangular arrangement requiring sufficient extent in width and length) with an inertial sensing system. Instead of relying on the physical geometry of markers to solve the exterior orientation problem, the system uses inertial sensors to directly measure orientation, eliminating the need for cumbersome triangular marker mounting on slender objects
3Productivity
If inertial sensors are used for continuous tracking, then productivity is improved, but measurement precision deteriorates due to drift
Solution Approach 1:
The patent implements a feedback mechanism where the single camera periodically captures images to provide absolute position references that correct accumulated drift from inertial sensors. The system continuously integrates inertial data for high-speed tracking while using periodic visual feedback from the camera to reset and correct orientation and position errors, maintaining both high productivity and measurement precision
Data Source
AI summary
The spatial location and azimuth of an object are computed from the locations, in a single camera image, of exactly two points on the object and information about an orientation of the object.One or more groups of four or more collinear markers are located in an image, and for each group, first and second outer markers are determined, the distances from each outer marker to the nearest marker in the same group are compared, and the outer marker with a closer nearest marker is identified as the first outer marker. Based on known distances between the outer markers and the marker nearest the first outer marker, an amount of perspective distortion of the group of markers in the image is estimated. Based on the perspective distortion, relative distances from each other point in the group to one of the outer markers are determined. Based on the relative distances, the group is identified.