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probabilistic_mapping's Introduction

This is the implementation of our paper: "Probabilistic Dense Reconstruction from a Moving Camera" in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018. Our implementation runs on Linux and is fully integrated with ROS (developed in version of indigo). Eigen, OpenCV 3+, and CUDA are required.

The high resolution video of our submitted paper is: https://1drv.ms/v/s!ApzRxvwAxXqQmlW9ZOrp9hdA7ude (720p is recommmanded)

To complie this code, you should modify the arch code (i.e. --generate-code arch=compute_XX,code=sm_XX) in the CMakeList.txt such that it adapts to your GPU.

This code is not well ready for the public, some hints for use are as follows:

The input of my code is curImg(current image), cur_R (rotation), cur_t (translation) and fx, fy, cx, cy (camera intrinsics) in the ‘evaluate.cc’ file. You can modify some lines between 240 and 260 of the ‘evaluate.cc’ file. motion_stereo_mapping.init_reference() and motion_stereo_mapping.update() calculate the cur depth image; motion_stereo_mapping.outFusion() outputs the fused depth image.

The source code is released under GPLv3 license. If you use our code, please cite our paper:

[1] Yonggen Ling, Kaixuan Wang and Shaojie Shen, "Probabilistic Dense Reconstruction from a Moving Camera" in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018.

For more questions, please contact ylingaa at connect dot ust dot hk .

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