Range Finder Network Calibration via Moving Object Tracking
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Solution Overview
Problem
Calibrating a network of multiple range finders, such as laser range finders, to determine their relative positions is challenging, especially when the overlap between their visible areas is small, and conventional methods require predefined calibration patterns or visual measurements.
Innovation Solution
The calibration method uses measurements of an object moving through the area covered by the range finders to determine relative transformations between them, allowing for the calculation of their poses without the need for special calibration patterns or camera images, using iterative optimization techniques and static analysis to establish a common coordinate system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If multiple range finders are used to survey larger areas, then the coverage area is improved, but the overlap between individual scanning ranges is reduced
Solution Approach 1:
A moving object serves as an intermediary element that enables calibration between range finders with limited overlap. The object is detected by multiple range finders at different positions and times, creating a bridge for establishing spatial relationships even when direct overlap between scanners is minimal.
Solution Approach 2:
The calibration method utilizes dynamic movement of an object through the scanning area to establish spatial relationships. By tracking the object's position across multiple scans from different range finders, the system can determine relative positions and orientations without requiring static calibration patterns or extensive overlap.
2Measurement precision
If conventional calibration algorithms with predefined patterns are used, then data association is improved, but the calibration process becomes more complex and time-consuming
Solution Approach 1:
The system performs self-calibration by automatically detecting and tracking a moving object to determine relative positions of range finders. The calibration process is autonomous and does not require external calibration patterns or manual intervention, significantly reducing calibration time while maintaining accuracy.
Solution Approach 2:
The method performs preliminary detection and tracking of a moving object to establish initial spatial relationships before final calibration. This preliminary action enables the system to quickly determine relative positions without requiring time-consuming iterative optimization with predefined patterns.
3Device complexity
If the number of sensors in the network is reduced for economic reasons, then device complexity is reduced, but the overlap between visible areas is reduced
Solution Approach 1:
A moving object acts as a mediator that enables accurate calibration even with minimal overlap between sensors. By detecting the object across different scanners, the system can establish precise spatial relationships without requiring extensive overlapping coverage or a large number of sensors.
Solution Approach 2:
The calibration method changes the approach from relying on spatial overlap to relying on temporal and positional parameters of a moving object. By tracking object position across time and space, the system can determine sensor relationships even when spatial overlap is minimal, allowing fewer sensors to achieve accurate calibration.
Data Source
AI summary
A method and a system for calibrating a network of multiple range finders are disclosed. In an embodiment, the method includes performing a measurement with the range finders during a movement of an object through an area covered by the network, identifying, for each range finder, data sets of the measurement associated with the moving object, based on a static analysis of the respective range finder, determining, for each pair of overlapping range finders, an estimated relative transformation between respective poses of the pair, based on the identified data sets of the pair, determining an initial maximum likelihood configuration of poses of all of the range finders based on the estimated relative transformations of each pair and iteratively determining sets of point correspondences of the identified data sets and if a distance of the points in the pair is below a threshold, the threshold being redefined decreasingly for each iteration.