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feat(centerpoint): voxel debug via grid map message #11673
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feat(centerpoint): voxel debug via grid map message #11673
technolojin
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This was referenced Nov 19, 2025
…U memory usage during NMS
…onality - Introduced `detectVoxelsDebug` method to visualize voxel generation without running inference. - Added `generateVoxelBoxes3D_launch` method for launching the CUDA kernel to create 3D boxes from voxel coordinates. - Updated `postProcess` to include voxel visualization logging and checks for the number of pillars.
…ion filtering - Added `is_valid_voxel` struct to filter out invalid boxes based on score. - Introduced visualization bounds for filtering boxes outside specified ranges. - Updated `generateVoxelBoxes3D_kernel` to use a minimal height for ground-level visualization. - Modified `generateVoxelBoxes3D_launch` to copy only valid boxes to the host, improving efficiency.
…ization methods - Deleted `detectVoxelsDebug` method and associated CUDA kernel for generating voxel boxes, as they are no longer needed. - Removed `generateVoxelBoxes3D_launch` method and its related kernel to streamline the codebase. - Updated visualization bounds in `pointCloudCallback` for improved range handling. - Adjusted occupancy mapping logic to reflect changes in visualization requirements.
…on support - Introduced `getVoxelGridData` method to retrieve voxel heights along with coordinates and point counts. - Implemented `computeVoxelHeights_launch` CUDA kernel to calculate the height of each voxel based on its points. - Updated `pointCloudCallback` to support visualization mode selection between occupancy and height. - Enhanced data handling for voxel heights in the visualization process, improving the representation of voxel data.
- Introduced `getVoxelGridData` method to retrieve voxel mean Z values along with coordinates, point counts, and voxel heights. - Implemented `extractFeatureMeanZ_launch` CUDA kernel to compute mean Z from encoded features. - Updated `pointCloudCallback` to include a new visualization mode for mean Z, enhancing data representation. - Enhanced memory management for voxel mean Z in the processing pipeline, improving overall efficiency.
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- Implemented transformation of the occupancy grid from the ego frame to the map frame using TF2. - Added error handling to fallback to the ego frame if the transformation fails. - Updated grid message header and origin position based on the transformed coordinates, enhancing spatial accuracy in occupancy mapping.
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component:perception
Advanced sensor data processing and environment understanding. (auto-assigned)
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