To address the high memory consumption of the global point cloud map generated by FastLIO2, consider implementing the following strategies:
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Downsampling the Point Cloud: Reduce the resolution or density of the point cloud using techniques like voxel grid filtering or random sampling. This minimizes data size while retaining sufficient navigational accuracy.
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Optimizing Data Structures: Transition to more memory-efficient data structures such as k-d trees for spatial partitioning, which can reduce both computational and memory complexity.
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Adjusting FastLIO2 Parameters: Explore configuration options within FastLIO2, such as limiting map size or setting thresholds for data pruning, to manage memory usage effectively.
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Implementing Dynamic Maps: In dynamic environments, adopt a dynamic mapping approach where only relevant data is stored and outdated information is discarded, freeing up memory.
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Efficient Data Storage Formats: Investigate more efficient storage formats or compression methods for the point cloud, such as sparse representations or encoding techniques that reduce data size.
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Algorithmic Optimizations: Review FastLIO2’s source code to identify inefficient memory allocations or leaks. Use profiling tools to isolate resource-intensive components and optimize accordingly.
By systematically applying these strategies, you can mitigate high memory usage in FastLIO2, ensuring smoother operation and improved exploration capabilities for the robot.