Correcting Past Position Errors in Mobile Object Tracking
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Solution Overview
Problem
Existing location tracking systems for mobile objects, such as containers, face challenges in achieving 100% accuracy due to obstacles and signal interference, leading to positioning errors that propagate and corrupt inventory databases, requiring manual correction and resulting in delays and costly measures.
Innovation Solution
A system and method that automatically corrects past position estimates using real-time data from positioning units, storing and calibrating past trajectories to improve accuracy, relying solely on current trip data without pre-stored maps or predetermined tracks, and integrating error correction into the tracking process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time positioning systems are used to track mobile objects, then location data can be obtained continuously, but positioning errors occur due to obstacles and signal interference
Solution Approach 1:
The system uses feedback from multiple sources including GPS, inertial navigation system (INS), and visual odometry to continuously correct positioning errors. The feedback loop processes sensor data, compares it with expected values, and adjusts position estimates in real-time to compensate for signal interference and obstacles.
Solution Approach 2:
The patent combines multiple positioning systems (GPS, INS, visual odometry) into an integrated navigation system. By merging the strengths of each system and using sensor fusion algorithms, the system achieves more reliable positioning than any single system could provide alone, compensating for the limitations of individual sensors.
2Measurement precision
If manual correction of position errors is performed, then accuracy can be improved, but operational delays and costs increase
Solution Approach 1:
The system performs self-correction of positioning errors through automatic algorithms that process sensor data and adjust position estimates without human intervention. The automated error correction mechanism continuously monitors positioning quality and applies corrections in real-time, eliminating the need for manual verification and reducing operational delays.
Solution Approach 2:
The positioning system operates continuously with uninterrupted error correction. The automated algorithms run continuously to process sensor data and correct position estimates, ensuring that accuracy is maintained at all times without the interruptions and delays associated with manual correction processes.
3Reliability
If complementary sensors are integrated to improve accuracy, then positioning reliability increases, but system complexity increases
Solution Approach 1:
The system uses a multi-functional integrated navigation platform that combines GPS, INS, and visual odometry systems. Each sensor serves multiple purposes: GPS provides global positioning, INS provides short-term accuracy and orientation, and visual odometry provides motion estimation. This multi-functionality allows the system to achieve high reliability through diverse data sources while managing complexity through unified processing.
Solution Approach 2:
The patent introduces an intermediary processing layer that mediates between multiple sensors and the final position output. This intermediary system (including sensor fusion algorithms and calibration modules) processes and coordinates data from multiple sensors, reducing the complexity of direct integration while maintaining the benefits of multi-sensor reliability.
Data Source
AI summary
A method is provided for calibrating past position estimates from a positioning system that provides real-time position estimates of a mobile object. The method first stores the real-time position estimates, which as time goes by become past position estimates and naturally form a first past trajectory depicting the past movement of the mobile object. Subsequently, a calibrated past trajectory is determined, which includes calibrated past position estimates that correspond to the same time instances as the past positions in the first past trajectory. When real-time positions have low qualities, this method calibrates them at a later time by using (higher-quality) real-time positions both before and after them. Errors in the past positions are then corrected based on the calibrated past trajectory. When used with event detectors that indicate inventory transactions, this method can correct position errors associated with inventory events so as to improve the performance of inventory tracking.