I spent some time this weekend implementing a couple of my ideas for improving the way the tracking code in OpenHMD filters and rejects (or accepts) possible poses when trying to match visible LEDs to the 3D models for each device.
In general, the tracking proceeds in several steps (in parallel for each of the 3 devices being tracked):
Do a brute-force search to match LEDs to 3D models, then (if matched)
Assign labels to each LED blob in the video frame saying what LED they are.
Send an update to the fusion filter about the position / orientation of the device
Then, as each video frame arrives:
Use motion flow between video frames to track the movement of each visible LED
Use the IMU + vision fusion filter to predict the position/orientation (pose) of each device, and calculate which LEDs are expected to be visible and where.
Try and match up and refine the poses using the predicted pose prior and labelled LEDs. In the best case, the LEDs are exactly where the fusion predicts they’ll be. More often, the orientation is mostly correct, but the position has drifted and needs correcting. In the worst case, we send the frame back to step 1 and do a brute-force search to reacquire an object.
The goal is to always assign the correct LEDs to the correct device (so you don’t end up with the right controller in your left hand), and to avoid going back to the expensive brute-force search to re-acquire devices as much as possible
What I’ve been working on this week is steps 1 and 3 – initial acquisition of correct poses, and fast validation / refinement of the pose in each video frame, and I’ve implemented two new strategies for that.
Gravity Vector matching
The first new strategy is to reject candidate poses that don’t closely match the known direction of gravity for each device. I had a previous implementation of that idea which turned out to be wrong, so I’ve re-worked it and it helps a lot with device acquisition.
The IMU accelerometer and gyro can usually tell us which way up the device is (roll and pitch) but not which way they are facing (yaw). The measure for ‘known gravity’ comes from the fusion Kalman filter covariance matrix – how certain the filter is about the orientation of the device. If that variance is small this new strategy is used to reject possible poses that don’t have the same idea of gravity (while permitting rotations around the Y axis), with the filter variance as a tolerance.
Partial tracking matches
The 2nd strategy is based around tracking with fewer LED correspondences once a tracking lock is acquired. Initial acquisition of the device pose relies on some heuristics for how many LEDs must match the 3D model. The general heuristic threshold I settled on for now is that 2/3rds of the expected LEDs must be visible to acquire a cold lock.
With the new strategy, if the pose prior has a good idea where the device is and which way it’s facing, it allows matching on far fewer LED correspondences. The idea is to keep tracking a device even down to just a couple of LEDs, and hope that more become visible soon.
While this definitely seems to help, I think the approach can use more work.
With these two new approaches, tracking is improved but still quite erratic. Tracking of the headset itself is quite good now and for me rarely loses tracking lock. The controllers are better, but have a tendency to “fly off my hands” unexpectedly, especially after fast motions.
I have ideas for more tracking heuristics to implement, and I expect a continuous cycle of refinement on the existing strategies and new ones for some time to come.
For now, here’s a video of me playing Beat Saber using tonight’s code. The video shows the debug stream that OpenHMD can generate via Pipewire, showing the camera feed plus overlays of device predictions, LED device assignments and tracked device positions. Red is the headset, Green is the right controller, Blue is the left controller.
Initial tracking is completely wrong – I see some things to fix there. When the controllers go offline due to inactivity, the code keeps trying to match LEDs to them for example, and then there are some things wrong with how it’s relabelling LEDs when they get incorrect assignments.
After that, there are periods of good tracking with random tracking losses on the controllers – those show the problem cases to concentrate on.