Untracked detection & tracked obstacle map publishing, integrating updated sensor fusion algorithm with the ObstacleMap interface #182
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…tected point clouds from the image to the point cloud.
…nto pliam1105/improved_perception
…de each detected object's bounding box (when projected onto the image), with the respective labels and probabilities, colcon build succesful, didn't test yet
…throws some out of range errors but I catched them and made them warnings and it seems to work fine)
…ions in resize, may be the wrong order in terms of channels so may need to change the order both in resize() and in at() again), seems to correctly select the points in the objects detected
…l meaningful clusters based on the detections
…on, need to find a euclidean clustering algorithm for n-dimensional data or with customizable distance function, or just one that takes color into account (there are some in PCL but they mostly rely on color thresholds or difference limits rather than adding their squared difference to the distance) or implement it from scratch (and KD-tree from scratch?)
…nce based condition)
… from the bounding box ones
… to publish it now
…izing them in RViz (not tested or built yet)
… test & troubleshoot/tune the matching
…formulas for the choosing & matching metrics
…nd those projected to cluster points)
…n the actual object, but more testing/tuning would be good)
… by publishing unorganized point clouds (channels don't matter anyways in visualization)
…le_detector.cpp that will serve the same purpose here) for untracked & tracked obstacle map publishing
…p format (builds, not tested)
…cleMap message format (and visualized in RViz appropriately)
…y and pretty consistent with occlusions/missing objects for some time, especially when out of camera FoV, though not perfect, may need fine-tuning)
…king and map publishing
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Publishes untracked (but color labeled) obstacle detections and tracked (using a simple euclidean distance object tracking as it was done until now, but also using the detected colors for matching the objects, the positions being the centroids of the published object point clouds) & labeled obstacle maps, including checking for obstacles that have been missing while on the FoV of the robot (by projecting the centroid to the camera plane) and removing them, but not removing obstacles that the robot just doesn't see because it's faced the other way, hopefully improving consistency. There are still duplicates sometimes, due to minor position changes, so I'm hoping to implement EKF (probabilistic) mapping (currently troubleshooting that) to mitigate that and hopefully have more consistent and accurate buoy positions and matching between detections.