Object Orientation Estimation Using Multi-Hypothesis Sensor Fusion
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Solution Overview
Problem
Autonomous robots face challenges in determining their orientation accurately, especially when equipped with accelerometers and gyroscopes, as they struggle to relate internal measurements with external localization systems, and existing solutions like magnetometers are prone to noise and external disturbances.
Innovation Solution
The method involves fusing measurements from internal and external coordinate frames using multiple hypotheses, where a state estimator like a Kalman Filter is used to compute the likelihood of each hypothesis, and the most likely orientation is selected, allowing for orientation estimation even in the absence of direct sensing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If magnetometers are used to determine orientation, then orientation information can be obtained, but the system becomes susceptible to external disturbances and noise
Solution Approach 1:
The patent introduces an intermediary computational framework that processes measurements from multiple sensors (accelerometers, gyroscopes, and optionally magnetometers) through a state estimator. This intermediary system reconciles conflicting sensor data by evaluating multiple hypotheses about orientation, thereby reducing the direct impact of noisy or disturbed magnetometer readings on the final orientation determination.
Solution Approach 2:
The patent creates a composite sensing approach by combining data from multiple sensor types (accelerometers, gyroscopes, and magnetometers) into a unified orientation estimation system. Rather than relying on a single sensor type, the system fuses measurements from different sensors with complementary characteristics, where accelerometers and gyroscopes provide reliable data in conditions where magnetometers fail, and magnetometers provide additional information when available and undisturbed.
2Measurement precision
If magnetometers are added to the robot, then orientation sensing capability is improved, but the weight and cost of the robot increase
Solution Approach 1:
The patent enables the robot to determine its own orientation using primarily self-contained inertial sensors (accelerometers and gyroscopes) combined with motion model-based hypotheses. The system serves its own orientation determination needs through computational fusion of inertial measurements and predicted motion dynamics, reducing or eliminating the need for external magnetometer hardware in many operating scenarios.
Solution Approach 2:
The patent replaces the magnetic sensing mechanism (magnetometers) with a computational-mechanical approach using accelerometers, gyroscopes, and state estimation algorithms. Instead of relying on magnetic field measurements, the system uses inertial measurements combined with mathematical models of robot motion to infer orientation, substituting a hardware-based magnetic sensing system with a software-based estimation system.
3Measurement precision
If multiple hypotheses are evaluated using state estimators, then orientation determination accuracy is improved, but computational resources are consumed
Solution Approach 1:
The patent implements a hypothesis evaluation strategy where not all possible hypotheses are processed to full completion. The state estimator evaluates multiple orientation hypotheses but can terminate the evaluation process early when sufficient confidence is achieved or when computational resources are constrained. This partial action approach maintains orientation accuracy while reducing unnecessary computational energy consumption.
Solution Approach 2:
The patent introduces dynamic adaptivity in the hypothesis evaluation process, where the number and complexity of hypotheses evaluated can change based on operational conditions. The system can adjust the level of hypothesis scrutiny based on factors such as sensor availability, motion dynamics, and computational resource status, making the computational energy consumption variable rather than fixed.
Data Source
AI summary
The system receives position information in both an internal coordinate frame and an external coordinate frame. The system uses a comparison of position information in these frames to determine orientation information. The system determines one or more orientation hypotheses, and analyzes the position information based on these hypotheses. The system may include on-board accelerometers, gyroscopes, or both that provide the measurements in the internal coordinate frame. These measurements may be integrated or otherwise processed to determine position, velocity, or both. Measurements in the external frame are provided by GPS sensors or other positioning systems. Position information is transformed to a common coordinate frame, and an error metric is determined. Based on the error metric, the system estimates a likelihood metric for each hypothesis, and determines a resulting hypothesis based on the maximum likelihood or a combination of likelihoods.