It would be good to test and compare the odometry performance with different combinations of sensors, including (1) stereo-VIO-only (2) stereo-VIO + GPS.
One option is to directly feed GPS into the UKF, the interfaces to support different sensors are all available in the UKF package included in this repo. However, this may cause problem if the difference between last UKF estimated pose and GPS estimated pose is too large (which can happen if you have intermittent GPS).
Alternatively, the GPS poses can be treated the same way as semantic SLAM estimated poses (publish odom-reference-frame to SLAM-reference-frame transform to compensate the odometry drift), however, we need to think about how to inform the UKF of this additional source of odometry, so that the covariance of UKF can be updated.
This will probably only work in situations where GPS is reliable enough.
versatran01
commented
2 years ago
It would be good to test and compare the odometry performance with different combinations of sensors, including (1) stereo-VIO-only (2) stereo-VIO + GPS.
One option is to directly feed GPS into the UKF, the interfaces to support different sensors are all available in the UKF package included in this repo. However, this may cause problem if the difference between last UKF estimated pose and GPS estimated pose is too large (which can happen if you have intermittent GPS).
Alternatively, the GPS poses can be treated the same way as semantic SLAM estimated poses (publish odom-reference-frame to SLAM-reference-frame transform to compensate the odometry drift), however, we need to think about how to inform the UKF of this additional source of odometry, so that the covariance of UKF can be updated.
This will probably only work in situations where GPS is reliable enough.