Stereo Visual Odometry using Kitti Dataset
- Optical flow
- GFTT
- Multi-threading, mutex locks
- Max active keyframes are taken to be 7. If active keyframes < 7, this has been taken care of.
Edge cases not taken care of
- Brightness not constant
- Lost tracking, reset function is not implemented
- Compare with other open source libraries
- I used GFTT as feature detector. This is not fast. Can use ORB, SIFT etc
- Taking care of edge cases, like changing brightness, lost tracking
- Used direct method for estimating pose and features for faster computation. Can compare with other techniques like OpticalFlow
- Can use pose graph instead of BA
- Add ways to quantify results
- Include features in major pipeline
- C++
cd ~/ws
mkdir build
cd build
cmake ..
make
Then to run on KITTI dataset
cd ~/ws
bin/run_kitti_stereo
0204 14:58:25.402386 29886 visual_odometry.cpp:41] VO is running
I0204 14:58:25.413861 29886 frontend.cpp:265] Find 130 in the last image.
I0204 14:58:25.415143 29886 frontend.cpp:211] Outlier/Inlier in pose estimating: 16/88
I0204 14:58:25.415163 29886 frontend.cpp:216] Current Pose =
000.559344 0-0.798614 000.222148 00-25.5681
000.825838 000.560017 0-0.066126 00-302.885
-0.0715975 000.220446 000.972768 00-356.362
0000000000 0000000000 0000000000 0000000001
I0204 14:58:25.420295 29886 visual_odometry.cpp:62] VO cost time: 0.00845767 seconds.
Output GIF of the visual odometry
- Use 3rd party libraries for version compatibility
- May need to run commands (or add in .bashrc):
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib
export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libffi.so.7
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