Surveying Instrument Localization Using VSLAM and 3D Matching
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Solution Overview
Problem
Existing surveying instruments face challenges in achieving accurate and reliable localization, particularly in automated systems, due to issues like drift and ambiguity, especially when used in environments with changing conditions or when GNSS accuracy is limited.
Innovation Solution
A method utilizing visual simultaneous location and mapping (VSLAM) algorithms with a movable surveying instrument to derive a sparse evolving point cloud, matched with a previously captured 3D geometry, minimizing distance functions to correct for drift and ensure accurate localization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If visual inertial SLAM systems are used for automated localization, then automation is improved, but reliability deteriorates due to drift and instability
Solution Approach 1:
The system continuously compares the evolving sparse point cloud with the pre-captured 3D geometry model, using the comparison results to correct localization drift in real-time. This feedback mechanism maintains reliability by constantly validating and adjusting the automated localization against a trusted reference model.
Solution Approach 2:
A complete 3D geometry model of the environment is captured and stored before the surveying instrument begins movement. This preliminary action creates a reliable reference framework that the instrument can continuously compare against during operation, ensuring stability without requiring continuous manual intervention.
2Productivity
If the surveying instrument moves along random trajectories with pauses, then productivity is improved, but localization accuracy deteriorates due to ambiguity and drift
Solution Approach 1:
The localization system operates continuously throughout the entire measurement process, constantly updating the sparse point cloud and comparing it with the reference 3D geometry. This continuous operation maintains accurate localization even during instrument movement and pauses, eliminating drift accumulation.
Solution Approach 2:
The reference 3D geometry model is captured in advance, creating a stable coordinate framework before any measurement activities begin. This preliminary mapping enables the instrument to maintain accurate localization throughout random movements and pauses by continuously referencing this pre-established model.
3Reliability
If manual localization methods are used, then reliability is improved, but ease of operation deteriorates due to tedious setup workflows
Solution Approach 1:
The system performs automated localization independently throughout the measurement process. The surveying instrument automatically captures images, updates the sparse point cloud, compares it with the reference 3D geometry, and corrects its own localization without requiring manual prism pole measurements or operator intervention for localization maintenance.
Solution Approach 2:
The manual mechanical localization process using prism poles and reference points is replaced with an automated optical and computational system. The instrument uses its own camera to capture environmental images and computational algorithms to maintain localization, eliminating the need for manual mechanical measurement procedures.
4Ease of operation
If GNSS is used for localization, then ease of operation is improved, but measurement precision deteriorates due to limited accuracy
Solution Approach 1:
The GNSS satellite-based localization system is replaced with a local visual-inertial SLAM system that uses the instrument's own camera and onboard sensors. This substitution provides both the ease of automated operation and the high precision required for surveying applications by creating a local reference frame independent of satellite signals.
Data Source
AI summary
A method for surveying an environment by a movable surveying instrument with a progressional capturing of 2D-images by at least one camera and applying a visual simultaneous location and mapping algorithm (VSLAM) or a visual inertial simultaneous location and mapping algorithm (VISLAM) with a progressional deriving of a sparse evolving point cloud of at least part of the environment, and a progressional deriving of a trajectory of movement. The method comprises a progressional matching of the sparse evolving point cloud with a previously derived 3D-geometry, with a minimizing of a function configured to model a distance between the sparse point cloud and the previously derived 3D-geometry and deriving a spatial localization and orientation of the surveying instrument. At least one surveying measurement value of the environment by a spatial measurement unit is combined with the sparse point cloud or the previously derived 3D-geometry.


