Data processor for pose estimation or visual odometry tasks.
- Download raw data from here.
- Download odometry data (color) from here.
- Copy the ground truth poses from
deepFEPE/datasets/kitti_gt_poses
.
`-- KITTI (raw data, odometry sequences, GT poses)
| |-- raw
| | |-- 2011_09_26_drive_0020_sync
| | | |-- image_00/
| | | `-- ...
| | |-- ...
| | `-- 2011_09_28_drive_0001_sync
| | | |-- image_00/
| | | `-- ...
| | |-- ...
| | `-- 2011_09_29_drive_0004_sync
| | | |-- image_00/
| | | `-- ...
| | |-- ...
| | `-- 2011_09_30_drive_0016_sync
| | | |-- image_00/
| | | `-- ...
| | |-- ...
| | `-- 2011_10_03_drive_0027_sync
| | | |-- image_00/
| | | `-- ...
| |-- sequences
| | |-- 00/
| | |-- ...
| | |-- 10/
| |-- poses
| | |-- 00.txt
| | |-- ...
| | |-- 10.txt
` ` `
WE ARE NOT FILTERING STATIC FRAMES FOR THE ODO DATASET!
Set --with_pose
--with_X
--with_sift
to decide whether to dump pose files, rectified lidar points, and SIFT kps/des and corres.
- Specify your
dataset_dir
anddump_root
python dump_tools/dump_data.py --dump --dataset_dir /media/yoyee/Big_re/kitti/data_odometry_color/dataset/ \
--dump_root /media/yoyee/Big_re/kitti/kitti_dump/odo_corr_dump_siftIdx_npy_delta1_test_0714 \
--with_pose --with_sift \
--img_height 376 --img_width 1241 --dataloader_name kitti_seq_loader --cam_id '02'
- Download raw data (Training data, Road11.tar.gz) from here or use the following script.
python dump_tools/apollo/download.py -h
python dump_tools/apollo/download.py --dataset_dir /media/yoyee/Big_re/apollo/train_seq_1 --if_download
# change the name to Road11.tar.gz
tar zxf Road11.tar.gz
- Specify your
dataset_dir
anddump_root
python dump_tools/dump_data.py --dump --dataset_dir /media/yoyee/Big_re/apollo/train_seq_1/ --dataloader_name apollo_train_loader --with_pose --with_sift --dump_root /media/yoyee/Big_re/apollo/apollo_dump/train_seq_1/ --cam_id 5 --img_height 2710 --img_width 3384
python dump_tools/euroc/download.py --dataset_dir /media/yoyee/Big_re/euroc/train_seq_1 --if_download --if_untar
- Match time stamps of rgb images, depth images and poses
python dump_tools/euroc/process_poses_euroc.py --dataset_dir /data/euroc/test2/ --dataset euroc
python dump_tools/dump_data.py --dump --dataset_dir /data/euroc/raw_sequence --with_pose --with_sift --dump_root /data/euroc/euroc_dump/slam_seq_v1 --cam_id 00 --img_height 480 --img_width 752 --dataloader_name euroc_seq_loader
python dump_tools/tum/download.py
python dump_tools/dump_data.py --dump --dataset_dir /data/tum/raw_sequences --with_pose --with_sift --dump_root /data/tum/tum_dump/slam_seq_v1 --with_X --cam_id 00 --img_height 480 --img_width 640 --dataloader_name tum_seq_loader
Refer to https://github.com/eric-yyjau/kitti_instance_RGBD_utils for some code snippets.
Please cite the following papers.
- DeepFEPE
This implementation is developed by You-Yi Jau and Rui Zhu. Please contact You-Yi for any problems.
DeepFEPE is released under the MIT License.