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LF-VISLAM: A SLAM Framework for Large Field-of-View Cameras with Negative Imaging Plane on Mobile Agents [PDF], IEEE T-ASE

Ze Wang, Kailun Yang, Hao Shi, Peng Li, Fei Gao, Jian Bai, Kaiwei Wang.

Wiki

The VIO system LF-VIO and LF-VIO Wiki is constantly being updated......

The Loop closure of LF-VISLAM will also be updated later.

If you find this work useful or interesting, please kindly give us a star ⭐, thanks!😀

How to run LF-VISLAM

1、Build LF-VIO on ROS Clone the repository and catkin_make:

    cd ~/catkin_ws/src
    git clone https://github.com/flysoaryun/LF-VISLAM.git
    cd ../
    catkin_make
    source ~/catkin_ws/devel/setup.bash

2、Run

    roslaunch vins_estimator mindvision.launch

Ourdoor test

LF-VISLAM_grass.mp4

Green: LF-VIO

Blue: LF-VISLAM

Red: Groundtruth

Download PALVIO Dataset

picture

ID01, ID06, ID10: Google Drive

ID01~ID10: Baidu Yun, Code: d7wq

IDL01~IDL02: Baidu Yun, Code: khw2

Dataset parameters

ID01~ID10: LF-VIO

IDL01~IDL02:

Pal_camera:

Fov: 360°x(40°~120°)

Resolution ratio: 1280x720

Lens: Designed by Hangzhou HuanJun Technology.

Sensor: mynteye module.

Frequency: 30Hz

Pal_camera:

model_type: scaramuzza
camera_name: pal
image_width: 1280
image_height: 720
poly_parameters:
   p0: -2.463825e+02
   p1: 0.000000e+00
   p2: 2.001880e-03
   p3: -3.074460e-06
   p4: 6.138370e-09
inv_poly_parameters:
   p0: 378.706255
   p1: 248.013259
   p2: 9.758682
   p3: 14.691543
   p4: 22.412646
   p5: 8.404425
   p6: -1.668530
   p7: 3.279720
   p8: 2.380985
   p9: -0.599038
   p10: 0.525474
   p11: 1.018587
   p12: 0.289189
   p13: 0.0
   p14: 0.0
   p15: 0.0
   p16: 0.0
   p17: 0.0
   p18: 0.0 
   p19: 0.0 
affine_parameters:
   ac: 0.999993
   ad: -0.000002
   ae: 0.000025
   cx: 605.933287
   cy: 362.393961

IMU(mynteye module):

Frequency: 200Hz
acc_n: 0.02          # accelerometer measurement noise standard deviation.
gyr_n: 0.04         # gyroscope measurement noise standard deviation.    
acc_w: 0.04         # accelerometer bias random work noise standard deviation.  
gyr_w: 0.002      # gyroscope bias random work noise standard deviation.

The extrinsic parameter between IMU and pal Camera

extrinsicRotation: !!opencv-matrix
   rows: 3
   cols: 3
   dt: d
   data: [-0.953122, 0.172069, 0.248899,
           0.134667, 0.977837, -0.160311,
           -0.270968, -0.119278, -0.95517]
extrinsicTranslation: !!opencv-matrix
   rows: 3
   cols: 1
   dt: d
   data: [-0.0324005, -0.00943501, 0.039072]

Publication

If you find this work useful, please consider referencing the following paper:

LF-VISLAM: A SLAM Framework for Large Field-of-View Cameras With Negative Imaging Plane on Mobile Agents

Z. Wang, K. Yang, H. Shi, P. Li, F. Gao, J. Bai, K. Wang.

IEEE Transactions on Automation Science and Engineering (T-ASE).

@article{LF-VISLAM,
  title={LF-VISLAM: A SLAM Framework for Large Field-of-View Cameras With Negative Imaging Plane on Mobile Agents},
  author={Wang, Ze and Yang, Kailun and Shi, Hao and Li, Peng and Gao, Fei and Bai, Jian and Wang, Kaiwei},
  journal={IEEE Transactions on Automation Science and Engineering},
  year={2023},
  publisher={IEEE}
}