dimensionalOS / dimensionalOS/dimos
Auto Scaling Global Voxel Grid Mapper
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- Dominant language
- Python
- Stars
- 4.5k
- Forks
- 808
- Avg merge
- 3d 5h
- Merged PRs (30d)
- 233
Description
For Go2 voxels.py mapper, we have a pre-set voxel grid size and injest frequency, injest frequency is basically lidar frame message frequency (I think set to 0)
This is an issue on weaker machines, rr.log (rerun log) lidar frame injest time to monitor this processing speed
And we should allow setting of ranges for scaling of processing frequency depending on how long the processing loop takes, no need to expect to re-process full res map on each lidar frame, if running non cuda on a shitty cpu - even 1hz (as opposed to 10hz) is fine
Propose the spec first here (for config changes) then when I confirm you can implement
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reading the Go2 voxels.py mapper and reviewing rr.log for lidar frame ingestion times. Define the proposed configuration changes for processing-frequency scaling based on loop duration and document the intended ranges and completion criteria. The issue requests maintainer confirmation of the specification before implementation.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- performance, robotics
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100