将QuadtreeMapping改成无ROS的版本，并利用TUM数据集进行测试

A Real-time Monocular Dense Mapping System

This is a monocular dense mapping system following the IROS 2018 Quadtree-accelerated Real-time Monocular Dense Mapping, Kaixuan Wang, Wenchao Ding, Shaojie Shen.

Requirement

• CUDA : version >8.0
• OpenCV : version > 3.0
• Eigen
• Boost

Dataset

We use TUM rgbd-slam dataset as data input. Link: http://vision.in.tum.de/data/datasets/rgbd-dataset/download

Tools

associate.py

1. It reads the time stamps from the rgb.txt file and the depth.txt file.
2. Joins them by finding the best matches.
3. Write them into the tuple.txt.
4. Also compare the time stamps from the rgb.txt file and the groundtruth.txt file then write them into tuple_ground.txt.

 python associate.py /home/robomaster/dataset/rgbd/rgb.txt /home/robomaster/dataset/rgbd/depth.txt 
 python associate.py /home/robomaster/dataset/rgbd/rgb.txt /home/robomaster/dataset/rgbd/groundtruth.txt

How to install

1. Install Eigen & OpenCV (if you don't have it):

sudo apt-get install libeigen3-dev libopencv-dev

2. install ziplib:

sudo apt-get install zlib1g-dev
cd thirdparty
tar -zxvf libzip-1.1.1.tar.gz
cd libzip-1.1.1/
./configure
make
sudo make install
sudo cp lib/zipconf.h /usr/local/include/zipconf.h

tupele_ground.txt file format

The format of each line is timestamp image_path timestamp tx ty tz qx qy qz qw

Configure CMakeLists.txt

cmake -D CMAKE_BUILD_TYPE=Release -D CMAKE_INSTALL_PREFIX=/usr/local/ -D WITH_TBB=ON -D BUILD_NEW_PYTHON_SUPPORT=ON -D WITH_V4L=ON -D WITH_QT=OFF -D WITH_OPENGL=ON -D OPENCV_EXTRA_MODULES_PATH=/home/robomaster/opencv_contrib/modules/ /home/robomaster/opencv-3.4.0/

cmake -D CMAKE_BUILD_TYPE=Release -D CMAKE_INSTALL_PREFIX=/usr/local/ -D OPENCV_EXTRA_MODULES_PATH=/home/robomaster/opencv_contrib/modules/ /home/robomaster/opencv-3.4.0/

Using OpenCL to build quadtree_image_kernel

The project refers to the following tutorials as well as its code.

1. HandsOnOpenCL slides
2. HandsOnOpenCL 中文翻译
3. HandsOnOpenCL 练习代码

Requirements

Export cpp_common files to the include path, especially header file util.hpp.

kernel function help list

Function	Property returned
uint get_work_dim()	The number of dimensions 维度统计
size_t get_global_id(uint dimidx)	The ID of the current work-item [0,WI) in dimension dimidx 读取该工作项在全局工作项范围内的id（输入参数为指定维度）
size_t get_global_size(uint dimidx)	The total number of work-items (WI) in dimension dimidx 读取该维度的工作项大小
size_t get_global_offset(uint dimidx)	The offset as specified in the enqueueNDRangeKernel API in dimension dimidx
size_t get_group_id(uint dimidx)	The ID of the current work-group [0, WG) in dimension dimidx 读取该工作项在该工作组范围内的id（输入参数为指定维度）？？？
size_t get_local_id(uint dimidx)	The ID of the work-item within the work-group [0, WI/WG) in dimension dimidx 读取该工作项在该工作组范围内的id（输入参数为指定维度）？？？
size_t get_local_size (uint dimidx)	The number of work-items per work-group = WI/WG in dimension dimidx
size_t get_num_groups(uint dimidx)	The total number of work-groups (WG) in dimension dimidx

How CUDA kernel function generate_gradient() work in code

Call hierarchy

DepthmapNode::readTumDataSet()
    ->Depthmap::add_frames()
        ->SeedMatrix::input_raw()
            ->SeedMatrix::add_frames()
                ->SeedMatrix::add_income_image()
                    ->generate_gradient()
                        ->__kernel__ generate_gradient()

What we need to do