LIDAR Grid Occupancy Mapping Using Error Range Distribution
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Solution Overview
Problem
Existing LIDAR-based mapping technologies face inaccuracies due to hardware measurement errors, particularly in low-cost systems, leading to incorrect laser point data and environment mapping.
Innovation Solution
The method involves using a LIDAR device to calculate occupation probabilities for grid cells in a 2D environment map by incorporating error range distributions associated with hardware measurement errors, employing bilinear and bicubic interpolation techniques to refine occupation probability calculations based on error ranges, and updating the environment map accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LIDAR hardware measurement errors are not accounted for, then the mapping process is simpler and faster, but the environment map accuracy deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-calculating error ranges for different distance intervals and storing them in a lookup table before the actual mapping process. During mapping, the system simply queries the pre-computed error ranges based on laser point distances, avoiding real-time complex error calculations and significantly reducing computational overhead while maintaining accuracy
Solution Approach 2:
The patent changes the parameter representation by transforming raw laser point data into occupation probabilities using error-range-aware interpolation. Instead of working with binary occupied/unoccupied grid cells, the system uses continuous probability values (0-1) that incorporate measurement uncertainty, allowing for more nuanced and accurate environment representation
2Measurement precision
If error range distribution is incorporated into mapping, then occupation probability accuracy is improved, but computational time increases
Solution Approach 1:
The patent pre-computes error ranges for multiple distance intervals and stores them in a lookup table before actual mapping operations. This preliminary preparation allows the mapping process to quickly retrieve appropriate error ranges without performing complex real-time calculations, significantly reducing computational time while maintaining accuracy
Solution Approach 2:
The patent applies partial action by selectively applying different interpolation methods (bilinear for small errors, bicubic for large errors) based on the magnitude of measurement errors. This selective approach avoids the computational overhead of always using the more complex bicubic interpolation, achieving a balance between accuracy and efficiency
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 enhances the accuracy of environment mapping by accounting for measurement errors, improving the reliability of robot pose determination and navigation in unknown environments.
Implementation Method 1
LIDAR (e.g., Light Detection And Ranging, or Light Imaging, Detection, And Ranging) is a technique used to measure a distance to a target by illuminating the target with laser light, and measuring reflected laser light detected using a laser sensor
Data Source
AI summary
A technology is described for mapping a physical environment. An example method may include receiving laser point data for laser light reflected from the physical environment and detected by a laser sensor. Points included in the laser point data can be correlated to grid cells in an environment map that represents the physical environment. Error ranges for the points correlated to the grid cells can be determined based in part on an error distribution. Occupation probabilities can then be calculated for the grid cells in the environment map using an interpolation technique and grid cell occupation probabilities for adjacent error grid cells selected based in part on the error ranges of the points, and the grid cells in the environment map can be updated with the occupation probabilities.