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vectr-ucla / direct_lidar_inertial_odometry

[IEEE ICRA'23] A new lightweight LiDAR-inertial odometry algorithm with a novel coarse-to-fine approach in constructing continuous-time trajectories for precise motion correction.
MIT License
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Intrinsics and extrinsics #27

Open ubicray opened 7 months ago

ubicray commented 7 months ago

Hey! Firstly, thanks for great paper and code! I used this tool for intrinsics and extrinsics: https://github.com/hku-mars/LiDAR_IMU_Init I need help on understanding how to implement those values for DLIO:

Initialization result: Rotation LiDAR to IMU (degree) = -0.084620 -0.649203 -1.151916 Translation LiDAR to IMU (meter) = -0.056023 -0.021736 0.026451 Time Lag IMU to LiDAR (second) = -0.010057 Bias of Gyroscope (rad/s) = -0.007985 -0.012948 0.020327 Bias of Accelerometer (meters/s^2) = 0.010086 0.009814 0.010098 Gravity in World Frame(meters/s^2) = -0.027753 0.030412 -9.809914

Homogeneous Transformation Matrix from LiDAR to IMU: 0.999734 0.020119 -0.011298 -0.056023 -0.020101 0.999797 0.001704 -0.021736 0.011330 -0.001477 0.999935 0.026451 0.000000 0.000000 0.000000 1.000000

Refinement result: Rotation LiDAR to IMU (degree) = -0.014112 -0.341810 -1.171677 Translation LiDAR to IMU (meter) = -0.042382 -0.019593 0.020312 Time Lag IMU to LiDAR (second) = -0.010057 Bias of Gyroscope (rad/s) = -0.008959 0.001204 0.013092 Bias of Accelerometer (meters/s^2) = 0.013954 0.009286 -0.020293 Gravity in World Frame(meters/s^2) = -0.029866 0.030184 -9.791937

Homogeneous Transformation Matrix from LiDAR to IMU: 0.999773 0.020448 -0.005959 -0.042382 -0.020446 0.999791 0.000368 -0.019593 0.005965 -0.000246 0.999982 0.020312 0.000000 0.000000 0.000000 1.000000

Btw, using livox mid-360