Parse /dvl/json_data and /dvs/imu to build ground truth.

[discoimp]

From the DVL we have the four individual beams and the calculated relative velocity in three axis.

Ultimate SLAM gives the following odometry data points:
header:
seq: 81792
stamp:
secs: 1684064391
nsecs: 693344992
frame_id: "map"
child_frame_id: "body"
pose:
pose:
position:
x: 23.04004746293359
y: 10.819543399286042
z: 0.9230756700943571
orientation:
x: 0.18399829106514815
y: -0.7461530401889032
z: 0.6335381547194111
w: -0.08960845951521128
covariance: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
twist:
twist:
linear:
x: -0.6439004046919854
y: -0.7267548781768475
z: 0.16027731232105638
angular:
x: 0.008871664033378907
y: -0.045741619248522845
z: -0.045414398053890824
covariance: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]

Create comparable ground truth

[discoimp]

Use the log file (.bin) from the Pixhawk to extract additional topics.
Find equivalent timestamps from the pressure or imu messages originating from the pixhawk to syncronise the logs.

[HikariS97]

@discoimp Hi~

Thanks for sharing the datasets. Can I ask where can I find the extrinsic parameters for DVL and pressure sensor relative to IMU?