Survey Pole Tilt Compensation Using MEMS and GNSS Fusion
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Solution Overview
Problem
Current surveying systems face challenges in accurately measuring the position of a point on the ground without leveling a survey pole, particularly due to errors in inertial sensors and the need for costly high-grade sensors, which are not feasible for industrial-grade applications.
Innovation Solution
A surveying system that combines low-cost MEMS inertial sensors with GNSS measurements using a Divided Difference Filter algorithm to accurately compensate for the tilt of the survey pole, allowing for precise position determination without the need for continuous GNSS signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-grade inertial sensors are used to accurately measure pole tilt, then measurement precision is improved, but device complexity and cost increase significantly
Solution Approach 1:
The patent combines low-cost MEMS inertial sensors with GNSS receiver data and uses a Kalman filter to fuse these measurements. This merging of multiple measurement sources compensates for the limitations of individual low-cost sensors, achieving accurate pole tilt measurement without requiring expensive high-grade inertial sensors alone.
Solution Approach 2:
The Kalman filter acts as an intermediary that processes and fuses data from low-cost MEMS sensors and GNSS receiver. It separates useful signal from noise and compensates for sensor errors, enabling accurate attitude determination without directly using high-grade sensors.
2Device complexity
If low-cost MEMS inertial sensors are used, then device complexity is reduced, but sensor errors and measurement precision deteriorate
Solution Approach 1:
The system uses feedback from the GNSS receiver to continuously monitor and correct errors in the MEMS inertial sensor measurements. The Kalman filter uses the GNSS position data as feedback to adjust and compensate for drift and biases in the low-cost sensor readings, maintaining accuracy over time.
Solution Approach 2:
The system dynamically adjusts measurement parameters and fusion weights based on signal quality and environmental conditions. The Kalman filter adapts its processing parameters to optimize the combination of MEMS and GNSS data, extracting maximum useful information while compensating for low-cost sensor limitations.
3Measurement precision
If the pole is leveled using traditional methods, then measurement precision is improved, but productivity decreases due to time consumption
Solution Approach 1:
The patent replaces the mechanical leveling process with an electronic/computational solution. Instead of physically adjusting the pole to be perfectly vertical using bubble levels, the system uses inertial sensors and computational algorithms to measure and compensate for pole tilt, allowing measurements to be taken in the pole's current orientation.
Solution Approach 2:
The system performs preliminary computational compensation for pole tilt before the actual position measurement is finalized. By pre-calculating the tilt compensation using inertial sensor data, the system eliminates the need for time-consuming physical leveling adjustments during the surveying process.
4Ease of operation
If electronic compass is used to determine azimuth, then ease of operation is improved, but measurement precision deteriorates due to magnetic field disturbances
Solution Approach 1:
The system uses the inertial sensors as an intermediary to determine azimuth without relying on magnetic field measurements. The gyroscopes and accelerometers provide a magnetic-field-independent reference frame that the system uses to calculate azimuth, avoiding the interference problems of electronic compasses while maintaining ease of operation.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables quick, reliable, and cost-effective measurement of ground positions by intelligently fusing inertial and GNSS data, reducing attitude drift and sensor errors, thus improving surveying accuracy and efficiency.
Implementation Method 1
an inertial measuring unit that is placed on the body with a defined spatial relationship relative to the position giving means and is designed in form of a micro-electro-mechanical system and comprises IMU-sensors including accelerometers and gyroscopes
Implementation Method 2
an inertial measuring unit that is placed on the body with a defined spatial relationship relative to the position giving means and is designed in form of a micro-electro-mechanical system and comprises IMU-sensors including accelerometers and gyroscopes
Implementation Method 3
A surveying system that combines low-cost MEMS inertial sensors with GNSS measurements using a Divided Difference Filter algorithm to accurately compensate for the tilt of the survey pole
Data Source
Figure 1a~1b
Figure 2
Figure 3~4
AI summary
Surveying system for measuring the position of a measuring point (1) on the ground, the surveying system comprising a survey pole (10) with a body (13) having a pointer tip (12) for contacting the measuring point (1) and position giving means for making available the coordinative determination of a referenced position, being placed on the body (13) with a defined spatial relationship relative to the tip (12), determination means for repeatedly determining the referenced position of the position giving means and evaluation means (17) for deriving the position of the measuring point (1), wherein the survey pole (10) further comprises an inertial measuring unit (18) placed on the body (13) with a defined spatial relationship relative to the position giving means, the surveying system further comprises IMU-processing means for repeatedly determining inertial state data based on measurements taken by the inertial measuring unit, and the evaluation means (17) are further configured for feeding a predefined filter algorithm with the repeatedly determined referenced position and the repeatedly determined inertial state data and deriving therefrom referenced attitude data for the survey pole (10), taking into account the defined spatial relationship of the inertial measuring unit (18) relative to the position giving means, using a DDF within the predefined filter algorithm, and further using the referenced attitude data for deriving the position of the measuring point (1).