Online Self-Calibration of 3D Measurement Sensors Using a Voxel-Based Network

Implementation of the paper “Online Self-Calibration of 3D Measurement Sensors Using a Voxel-Based Network”. paper link

Problem

There is a problem with equation (1) in the paper.
"Y = base*(v-c_v)/disp" is incorrect. . . .
"Y = f_u*base *(v-c_v)/(f_v *disp)"is correct. . . .
And no explanation is provided for w and h in Equation (2) in the paper.
w is 2048 and h is 128.

I'm sorry for the inconvenience. But the code in this repository is fine.

Weights

kitti-20 kitti-10 kitti-5 kitti-2 kitti-1
oxford-20 oxford-10 oxford-5 oxford-2 oxford-1

Setup

Download the kitti odometry dataset.
Replace all calib.txt in data folder with calib.txt downloaded from "Download odometry data set (calibration files, 1 MB)".
(('Tr') line does not exist, giving an error.)
Download the oxford radar robotcar dataset.
Download left LiDAR and right LiDAR.
Download Bumblebee XB3 Visual Odometry.
I used 2019-01-10-12-32-52 for training and 2019-01-17-12-48-25 for testing.

Open the cfg files in the CFG folder and modify the following two paths.
cfg.odometry_home = '/home/jnu-ie/Dataset/odometry/'
cfg.proj_home = '/home/jun-ie/kew/calib-using-voxel/'

Requirement

python >= 3.9.7. I worked in an anaconda environment.
pytorch >= 1.10.2 (https://pytorch.org/)
spconv >= 2.1.21 (https://github.com/traveller59/spconv)
opencv (pip install opencv-python)
pykitti (pip install pykitti)
easydict (pip install easydict)
numba >= 0.55.1 (pip install numba)
matplotlib (pip install matplotlib)

GPU with more than 11 GB of memory

Run

Check out the readme.md in /scripts

Results

Special Thanks

I got a lot of help from the public implementation of LCCNet and CIA-SSD.
Thanks to LvXudong-HIT and Vegeta2020.