You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
We have a pretty serious problem on NaviGator which affects our obstacle avoidance ability: the course if filled with black buoys which we are penalized for colliding with. If they our detected, we can easily plan a trajectory around them. Unfortunately, our object detection system is entirely LIDAR based, which does not get good returns of black objects. We often only get 1 return off a buoy, which is filtered out as it is difficult to distinguish this from normal erroneous water reflections at the same height. It seems pretty clear that we need to use computer vision here. This is my idea:
for any potential buoys (few returns at a low height), use the 3D points to find a region of interest in the nearest camera to check if it is a buoy or just water
use computer vision (traditional or machine learned) to return if this point should be kept
Neural network ???
Profit
The text was updated successfully, but these errors were encountered:
Another interesting idea is using the camera we found that seems to be responsive to UV spectrum. Perhaps the buoys would look distinct against the background with this camera.
We have a pretty serious problem on NaviGator which affects our obstacle avoidance ability: the course if filled with black buoys which we are penalized for colliding with. If they our detected, we can easily plan a trajectory around them. Unfortunately, our object detection system is entirely LIDAR based, which does not get good returns of black objects. We often only get 1 return off a buoy, which is filtered out as it is difficult to distinguish this from normal erroneous water reflections at the same height. It seems pretty clear that we need to use computer vision here. This is my idea:
The text was updated successfully, but these errors were encountered: