DATA ACQUISTION AND INPUT OF NEURAL NETWORK METHOD FOR DEEP ODOMETRY ASSISTED BY STATIC SCENE OPTICAL FLOW
A method of visual odometry for a non-transitory computer readable storage medium storing one or more programs is disclosed. The one or more programs comprise instructions, which when executed by a computing device, causes the computing device to perform the following steps comprising: performing data alignment; obtaining data from sensors; based on the data from the sensors, performing machine learning in a visual odometry model; generating a prediction of static optical flow; generating motion parameters; and training the visual odometry model by using at least one of the prediction of static optical flow and the motion parameters.
1 . A method of visual odometry for a non-transitory computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, causes the computing device to perform the following steps comprising:
performing data alignment;
obtaining data from sensors;
generating a prediction of static optical flow;
generating motion parameters; and
training a visual odometry model by using at least one of the prediction of static optical flow and the motion parameters.
2 . The method according to claim 1 , wherein performing data alignment comprises:
calibrating intrinsic parameters of a camera; and
calibrating extrinsic parameters between the camera and an inertial navigation module.
3 . The method according to claim 2 , wherein the inertial navigation module includes a global navigation satellite system (GNSS)-inertial measurement unit (IMU) or an IMU-global positioning system (GPS) module.
4 . The method according to claim 1 , wherein obtaining data comprises:
obtaining images from a camera; and
obtaining point clouds from a LiDAR.
5 . The method according to claim 4 further comprising:
obtaining vehicle poses from an inertial navigation module.
6 . The method according to claim 4 , wherein the camera includes a monocular camera or a stereo camera, and the images include RGB images or RGB images with depth information.
7 . The method according to claim 1 , wherein generating a prediction of static optical flow comprises:
extracting representative features from a pair input images;
generating a first flow output having a first resolution; and
generating a second flow output having a second resolution higher than the first resolution.
8 . The method according to claim 7 , after extracting, further comprising:
merging the extracted representative features; and
decreasing the merged features in feature map size.
9 . The method according to claim 8 further comprising:
merging the first flow output and the decrease features to generate a motion estimate.
10 . The method according to claim 9 further comprising:
generating the motion parameters based on the motion estimate.