LiDAR Environment Mapping with Local and Global Trajectory Correction
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Solution Overview
Problem
Existing methods for constructing environment maps using lidar data are prone to inaccuracies due to subjective and imprecise identification of flat zones, leading to blurred maps, as the trajectory of the measuring device is not accurately known, and the thickness criterion for identifying flat areas is unreliable.
Innovation Solution
A system and method that pre-selects initial point sets from the point cloud, applies local corrections to determine actual areas of interest, and performs global corrections to the measuring device's trajectory, using optimization problems to ensure accurate identification of flat and cylindrical zones, thereby enhancing the map's clarity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the thickness criterion is used to identify flat zones, then the identification process is simplified, but the precision and reliability of zone identification deteriorates
Solution Approach 1:
The patent changes the identification parameter from a single thickness criterion to multiple geometric parameters including thickness, planarity, and area. This allows the system to maintain ease of operation while improving identification precision by evaluating zones against multiple criteria rather than a single threshold.
Solution Approach 2:
The patent performs preliminary classification of zones into flat, cylindrical, and other types before detailed analysis. This preliminary action filters the point cloud data to focus computational resources on identifying characteristic features of each zone type, improving both precision and efficiency.
2Measurement precision
If the predefined thickness is set too low, then the precision of flat area identification is improved, but the quantity of usable flat areas decreases
Solution Approach 1:
The patent introduces multiple parameters (thickness, planarity, area) to replace the single thickness parameter. This allows the system to identify flat areas with appropriate precision while maintaining a sufficient quantity of usable zones by adjusting the combined criteria rather than relying solely on a low thickness threshold.
Solution Approach 2:
The patent evaluates multiple geometric properties beyond the minimum required thickness criterion. By assessing planarity, area, and other characteristics in addition to thickness, the system can identify zones that partially meet traditional criteria while still being useful for mapping, thus increasing the quantity of usable flat areas.
3Quantity of substance
If the predefined thickness is set too high, then the quantity of flat areas is increased, but the precision of identification deteriorates
Solution Approach 1:
The patent replaces the single thickness parameter with a combination of parameters including thickness, planarity, and area. This multi-parameter approach allows the system to increase the quantity of identified flat areas while maintaining precision by ensuring that zones meet multiple criteria simultaneously rather than relying on a single high thickness threshold.
4Extent of automation
If automatic mapping is performed without defining zones of interest, then the automation level is improved, but the accuracy of trajectory estimation deteriorates
Solution Approach 1:
The patent enables the mapping system to automatically identify and classify zones of interest (flat, cylindrical, etc.) from the point cloud data without requiring external input. The system serves itself by autonomously determining which zones to use for trajectory estimation, maintaining high automation while improving accuracy through intelligent zone selection.
Solution Approach 2:
The patent implements feedback mechanisms where the identified zones of interest are used to refine trajectory estimation, which in turn improves the accuracy of zone identification. This iterative feedback loop allows automatic mapping to achieve high precision trajectory estimation by continuously improving zone selection based on preliminary trajectory results.
5Measurement precision
If the trajectory correction is performed iteratively, then the precision of trajectory estimation is improved, but the computation time increases
Solution Approach 1:
The patent performs preliminary classification of zones and preliminary trajectory estimation before iterative refinement. This preliminary action prepares the data structures and identifies key zones in advance, reducing the computational burden of subsequent iterative corrections and allowing faster convergence to precise trajectory estimates.
Solution Approach 2:
The patent segments the trajectory correction process into distinct phases: preliminary zone identification, initial trajectory estimation, and iterative refinement. By segmenting the computation, the system can optimize each phase independently and terminate the iterative process when sufficient precision is achieved, reducing overall computation time.
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 allows for real-time, accurate identification of areas of interest, reducing computation time and ensuring a clear, precise environment map by correcting the measuring device's trajectory and speed, thus overcoming the limitations of previous methods.
Implementation Method 1
a measuring device, mobile in the environment, comprising at least one lidar. The lidar scans the environment in which the device is moving, acquiring point clouds representative of the environment
Data Source
AI summary
A system for constructing a map of an environment comprises a measuring device comprising at least one lidar acquiring a point cloud representative of said environment, a processor configured to pre-select a plurality of initial point sets from the point cloud, each initial point set being representative of a potential area of interest in the environment, or each initial point set, investigate a local correction to be made to a movement of the measuring device along each potential area of interest to determine whether each initial point set actually represents an area of interest, correct together the initial sets of points actually representing an area of interest, by applying a global correction to a movement of the measuring device in the environment, build the map of the environment with the corrected initial point sets and the initial point sets not representing an area of interest.