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superpoint-gtsam-vio's Introduction

superpoint-gtsam-vio

Visual Inertial Odometry using SuperPoint and GTSAM

Installation:

After cloning the repo:

#!bash
$ git submodule update --init --recursive
$ python3 -m venv env
$ source env/bin/activate
$ pip install -r requirements.txt

Usage:

Download the raw + synchronized KITTI data from here and the annotated depth map data set from here.

Run Visual-Inertial Odometry for e.g. date 2011_09_26 and drive 0022, skipping every 10th frame and using the first 701 frames available using the following command:

#!bash
$ python src/main.py --basedir /path/to/kitti/raw/data --date 2011_09_26 --drive 0022 --n_skip 10 --n_frames 701

VIO vs IMU-only vs Ground Truth

superpoint-gtsam-vio's People

Contributors

alextsolovikos avatar kristenmichaelson avatar

Stargazers

avatar Yash Turkar avatar Amin Abouee avatar avatar avatar avatar Huayan Zhang avatar avatar Dhruv Parikh avatar UJAS MANDAVIA avatar Zhiyong Wang avatar FredericBRHong avatar Sriram Vaikundam avatar Prashant Dandriyal avatar aqiuxx avatar Matthew avatar Jiahao Chen avatar maky avatar avatar yinloonga avatar avatar avatar 柒者 avatar Zhili avatar Tomato1107 avatar 赵焕峰 avatar avatar Catalina avatar avatar Lihong Jin avatar avatar avatar Sean avatar

Watchers

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Forkers

roger-chuh chengwei920412 sram-v jiajiaandwu zhanghua7099 flyinggh

superpoint-gtsam-vio's Issues

Origin of transformation matrices in `add_keypoints()` in `class VisualInertialOdometryGraph(object)`

Hi,

Kudos to your efforts. I am trying to understand your class VisualInertialOdometryGraph and was struck at grasping the origin of the transformation matrices:

      R_rect = np.array([[9.999239e-01, 9.837760e-03, -7.445048e-03, 0.],
                         [ -9.869795e-03, 9.999421e-01, -4.278459e-03, 0.],
                         [ 7.402527e-03, 4.351614e-03, 9.999631e-01, 0.],
                         [ 0., 0., 0., 1.]])
      R_cam_velo = np.array([[7.533745e-03, -9.999714e-01, -6.166020e-04],
                             [ 1.480249e-02, 7.280733e-04, -9.998902e-01],
                             [ 9.998621e-01, 7.523790e-03, 1.480755e-02]])
      R_velo_imu = np.array([[9.999976e-01, 7.553071e-04, -2.035826e-03],
                             [-7.854027e-04, 9.998898e-01, -1.482298e-02],
                             [2.024406e-03, 1.482454e-02, 9.998881e-01]])

Also, it would be very helpful if you could share some method to visualise the graph. I try to use graph.saveGraph('a.dot',self.initial_estimate) but get an incomplete graph as output.

Thanks

More information about this work

Hi,your work is excellent! Is there an article about this work? And I want to know some information of the project_depth dataset!
Thanks!

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