Material Handling Vehicle LiDAR Filtering With Voxel Occupancy Grids
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Solution Overview
Problem
Current hardware systems struggle to efficiently process large amounts of data generated by 3D LiDAR sensors in material handling vehicles, limiting the functionality of SLAM and obstacle detection systems.
Innovation Solution
Implementing a voxel grid filter with a density-based and distance-based approach to reduce the data points, marking cells as occupied or unoccupied based on threshold values adjusted by distance from the sensor, and representing each occupied cell with a centroid point.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 3D LiDAR sensors are used for SLAM or obstacle detection, then measurement precision and detection capability are improved, but the quantity of data generated increases significantly, causing current hardware systems to struggle with processing efficiency
Solution Approach 1:
The patent divides the 3D space into discrete voxel grid cells, segmenting the continuous point cloud data into manageable discrete units. Each voxel cell independently processes data points, allowing parallel computation and reducing the computational burden on hardware systems while maintaining detection accuracy.
Solution Approach 2:
The patent transforms the problem from processing individual 3D points to processing 3D volumetric cells (voxels). This dimensional transformation allows the system to aggregate multiple points into single cells, reducing data volume while preserving spatial information through the cell occupancy status and centroid calculations.
2Reliability
If all data points from the sensor are processed, then detection accuracy is maintained, but processing time and computational resources increase, reducing productivity
Solution Approach 1:
The patent extracts only the essential information from the point cloud data by determining cell occupancy status (occupied or unoccupied) and calculating centroid points for occupied cells. This extraction process discards redundant point-level details while retaining the critical spatial information needed for obstacle detection, significantly reducing processing requirements.
Solution Approach 2:
The patent changes the representation parameters from individual point coordinates to cell-level occupancy booleans and centroid positions. This parameter transformation reduces the data structure complexity and volume, enabling faster processing while maintaining the reliability needed for safety-critical obstacle detection applications.
3Ease of manufacture
If a fixed threshold value is used for marking cells as occupied, then the system is simple to implement, but it cannot adapt to varying distances from the sensor, reducing adaptability
Solution Approach 1:
The patent implements a dynamic threshold system where the occupancy threshold varies based on the distance of each voxel cell from the sensor. Cells at different ranges have different threshold values, allowing the system to adapt to the natural attenuation of sensor returns with distance. This dynamic adjustment maintains detection sensitivity across varying ranges while preserving relative computational simplicity.
Solution Approach 2:
The patent applies different threshold criteria to different spatial regions (voxel cells) based on their distance from the sensor. Rather than using a uniform threshold throughout the workspace, the system tailors the occupancy determination criteria to local conditions, with closer cells requiring different thresholds than distant cells, improving overall system adaptability.
Data Source
AI summary
A method of filtering a data set generated by a sensor on a material handling vehicle includes receiving a series of data points from the sensor on the material handling vehicle, mapping each data point in the series of data points to a cell of a voxel grid, determining a number of data points within each cell of the voxel grid, and comparing the number of data points within each cell of the voxel grid to a threshold value to reduce the number of data points. When the number of data points within a cell of the voxel grid is below the threshold value, the cell of the voxel grid is marked as unoccupied. When the number of data points within a cell of the voxel grid is equal to or above the threshold value, the cell of the voxel grid is marked as occupied.