EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Lobsters·Aug 10·5 min read
We're not done with point clouds
TL;DR
A new data structure for collision-checking against point clouds has been developed, significantly improving speed and memory efficiency. This advancement enhances motion planning for robots operating in complex environments.
✦ Why It Matters
Explore the new collision-checking data structure to enhance your robot's motion planning capabilities today.
Key Takeaways
- Explore using multilevel voxel tables for efficient collision detection.
- Benchmark voxel sizes to optimize performance for specific robots.
- Consider mutable MVTs for scenarios requiring dynamic updates.
- Evaluate the trade-offs between speed and safety in motion planning.
How It Works
MVTs divide space into a grid of voxels, each containing a list of points, eliminating the need for point duplication. This structure allows for quick identification of which voxel a query sphere belongs to, facilitating efficient collision checks using SIMD parallelism.