Camera-IMU Positioning with Stationary Bias Self-Calibration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional positioning devices face challenges in accurately measuring the position of moving bodies due to bias errors in detection signals from inertial measurement units (IMUs) without requiring external anchors, which incur high installation costs and complicate configurations.
Innovation Solution
A positioning device that includes a camera and an IMU, with a controller processing detection signals using correction values to remove bias errors, and performs stationary determination to update bias errors in real time, eliminating the need for external anchors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If external anchors are installed to calibrate bias errors, then measurement precision is improved, but device complexity and installation cost increase
Solution Approach 1:
The system performs self-calibration by detecting stationary periods during normal operation and automatically updating bias error correction values, eliminating the need for external anchors and manual calibration procedures. The moving body itself provides the calibration opportunity through its own operational patterns.
Solution Approach 2:
The system proactively detects stationary periods and updates bias errors in advance before they accumulate significant measurement deviations. By continuously monitoring for stationary conditions and performing timely calibration, the system maintains high positioning accuracy without requiring external reference points.
2Measurement precision
If external anchors are installed to remove bias errors, then measurement precision is improved, but installation cost increases
Solution Approach 1:
The system eliminates the need for expensive external anchor installations by performing self-calibration using its own operational data. The moving body calibrates itself during stationary periods encountered in normal operation, removing the requirement for costly infrastructure installation.
Solution Approach 2:
The system replaces expensive, permanent anchor installations with a software-based calibration approach that uses temporary stationary periods during operation. This software solution is far cheaper than physical anchor installations while achieving the same calibration objective.
3Measurement precision
If bias error correction is performed continuously, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The system performs bias error correction periodically during detected stationary periods rather than continuously. By updating calibration only when the moving body is stationary, the system maintains measurement precision while significantly reducing computational energy consumption compared to continuous correction.
Solution Approach 2:
The calibration frequency dynamically adapts to the operational state of the moving body. The system monitors motion status and activates calibration only during stationary periods, making the energy consumption variable rather than fixed, thereby optimizing the balance between accuracy and energy usage.
Data Source
AI summary
A positioning device includes an camera, a detector, and a circuit. The camera is mounted on a moving body, and captures an image of surroundings of the moving body to acquire a captured image. The detector is mounted on the moving body, detects motion of the moving body, and outputs a detection signal indicating a detection result. The circuit processes the detection signal using a correction value for correcting a bias error included in the detection signal without depending on the motion of the moving body. The circuit computes the position of the moving body based on the captured image acquired by the camera and the detection signal processed. If the circuit determines that the moving body is stationary, the circuit updates the correction value of the bias error based on the detection signal output by the detector.