search

Triton-AI / robocar-simple-line-follower

0 stars 0 forks source link

Research into Alternative Localization Methods #7

Open chenyenru opened 9 months ago

chenyenru commented 9 months ago

Hi Sid, thank you for pointing us to these sources!

I have summarized your points, could you confirm if I understood them correctly?

  • What is less important: Planning (can be improved tho)

    • The team's primary challenge is localizing at speed in a two-car race scenario.

    • Planning is simplified with a choice between 2-3 lines for the car to follow.

    • We don't need complex planning.

    • Pure pursuit controls are commonly used by winning teams but can be improved.

  • What is more important: Accurate Localization

    • Accurate localization is essential to avoid constant retuning and optimize performance.

Lidar, camera, or combined localization methods are critical for speed.

  • UCSD typically uses Lidars with frequencies of 10-20 Hz, while UPenn teams employ 40Hz Lidars for reduced distortion at high speeds.

  • Examples to look to for localization

  • Ideas for doing accurate localization with our constraints on LiDAR

    • Integrate IMU data for short-term position tracking and distortion correction could enhance localization.

    • Despite limited exploration in the F1Tenth community, cameras show potential for localization.

    • Exploring how OAK-D depth camera's depth capability could help

    • Research in drone racing suggests using Camera + IMU solutions as a promising starting point for further investigation.

    • Look into drone racing research for potential Camera + IMU solutions

Recommendation from @sisaha9 + some own research #1

Branching the issue to here to make topics organized.

chenyenru commented 9 months ago

Summary from chat with Computer Vision Professor Chandraker @ UCSD