Introduction

Build

requirements

Basic

## basic depencencies
sudo apt install cmake \
  libboost-dev \
  libeigen3-dev \
  libpng-dev \
  libgoogle-glog-dev \
  libatlas-base-dev

# SuiteSparse and CXSparse (optional)
# - If you want to build Ceres as a *static* library (the default)
#   you can use the SuiteSparse package in the main Ubuntu package
#   repository:
sudo apt-get install libsuitesparse-dev
# - However, if you want to build Ceres as a *shared* library, you must
#   add the following PPA:
sudo add-apt-repository ppa:bzindovic/suitesparse-bugfix-1319687
sudo apt update
sudo apt install libsuitesparse-dev

ROS

You can refer to http://wiki.ros.org/kinetic/Installation/Ubuntu for more information for installing ROS kinetic or higher version. This repo has been tested in kinetic and melodic.

PCL

## tested in pcl-1.7 (ubuntu16.04) and pcl-1.8 (ubuntu18.04)
## pcl-1.8 or higher version is strongly recommended
sudo apt install libpcl-dev

GTSAM

## Go to your wordspace lisk /home/user/3rd_parties
## GTSAM(4.0 or higher is needed)
git clone https://bitbucket.org/gtborg/gtsam.git
cd gtsam 
mkdir build && cd build
ccmake ..
### important
# set GTSAM_USE_SYSTEM_EIGEN to ON
# then generate makefile
make -j8
sudo make install

Ceres Solver

you can just install with apt

sudo apt install libceres-dev

or compile with source code

## Ceres Solver(1.12 or higher)
git clone https://github.com/ceres-solver/ceres-solver.git
cd ceres-solver
git checkout tags/1.14.0
mkdir build && cd build
cmake ..
make -j8
sudo make install

libnabo

git clone https://github.com/ethz-asl/libnabo.git
cd libnabo
### checkout to the latest release version
git checkout tags/1.0.7
mkdir build && cd build
cmake ..
make -j8
sudo make install

libpointmatcher

git clone https://github.com/ethz-asl/libpointmatcher.git
cd libpointmatcher
mkdir build && cd build
cmake ..
make -j8
sudo make install

Optional libs

CUDA: We have made some attempts in fasting the kdtree in ICP by creating the kdtree on GPU, but the GPU Kdtree is not fast enough(just 1.5~2 times faster than libnabo). Notice that if you use CUDA, your g++ version should be lower than 6.0 because the nvcc does not support the 6.0 or high version g++.
cuda_utils:
TBB: We have used concurrency containers in TBB for many multi-thread situations, if you turn off this options, the process will use stl containers such as std::vector instead and many multi-thread algorithm will degenerate into single-thread. So, Using TBB is strongly recommended;
OpenCV: All the matrices in code is in Eigen way, Opencv is only for generating the jpg file of pose gragh. It is a debug function so you can change this option as you wish.

compiling

mkdir build && cd build
cmake ..
make -j8
sudo make install

BUG

DO NOT use g++ 7.x, unknow bug with Eigen

How to use?

step1 run the mapping process

mkdir pcd
mkdir -p pkgs/test
./mapping.sh

before that, you should kown what is in the script:

## usally, you can leave this config file just like this, it will work fine
CONFIG_PATH=./config/static_mapping_default.xml
## the follow 2 items must be set!!!
## the topic name of your pointcloud msg (ros)
POINT_CLOUD_TOPIC=/fused_point_cloud
## the frame id of your pointcloud msg (ros)
POINT_CLOUD_FRAME_ID=base_link

## the following items are optional
## if you do not have a imu or gps or odom
## just remove the line started with
## imu: -imu -imu_frame_id
## odom: -odom -odom_frame_id
## gps: -gps -gps_frame_id
## and If you got one of these topics
## you MUST provide the tf connection between the one to pointcloud frame
IMU_TOPIC=/imu/data
IMU_FRAME_ID=novatel_imu

ODOM_TOPIC=/navsat/odom
ODOM_FRAME_ID=novatel_odom

GPS_TOPIC=/navsat/fix
GPS_FRAME_ID=novatel_imu

./build/static_mapping_node \
  -cfg ${CONFIG_PATH} \
  -pc ${POINT_CLOUD_TOPIC} \
  -pc_frame_id ${POINT_CLOUD_FRAME_ID} \
  -imu ${IMU_TOPIC} \
  -imu_frame_id ${IMU_FRAME_ID} \
  -odom ${ODOM_TOPIC} \
  -odom_frame_id ${ODOM_FRAME_ID} \
  -gps ${GPS_TOPIC} \
  -gps_frame_id ${GPS_FRAME_ID} 

exit 0

step2

play bag that includes pointcloud msgs or run the lidar driver

step3

when finished, just press 'CTRL+C' to terminate the mapping process. NOTICE that the mapping process will not end right after you 'CTRL+C', it has many more calculations to do, so just wait.
Finally, you will get a static map like this:

part of it:

Document

You can use doxygen Doxyfile to generate your docs, they are in the doc folder.

References

M2DP: A Novel 3D Point Cloud Descriptor and Its Application in Loop,Li He, Xiaolong Wang and Hong Zhang,IEEE International Conference on Intelligent Robots and Systems (2016) 2016-November 231-237
ISAM2: Incremental smoothing and mapping using the Bayes tree,Michael Kaess, Hordur Johannsson, Richard Roberts, Viorela Ila, John Leonard, and Frank Dellaert Abstract, International Journal of Robotics Research (2012) 31(2) 216-235
Comparing ICP Variants on Real-World Data Sets, Autonomous Robots 2013
Fast Segmentation of 3D Pointcloud for Ground Vehicles, M. Himmelsbach and Felix v. Hundelshausen and H.-J. Wuensche, IEEE Intelligent Vehicles Symposium, Proceedings, 2010

TODO

filtering the cars directly at the input of the pointclouds
add imu integrating factor in backend
improve motion filter
culling data structures like ImuMsg, NavSatMsg, etc.
add tests
fix motion compensation when motion filtering happens
lidar motion compensation after optimization
lidar motion compensation inside ICP
ICP using GPU
stand-alone ICP without libpointmatcher
parallel PointCloudLib
finish multi-trajectory map builder
improve MRVM
use ground detection to label the pointcloud
use some machine learning or deep learning method to add semantic labels
use a docker to release the environment
get odom message from a cheap GPS and IMU intergration
fix bug in imu pre-integration (now the imu is just for INS but not normal IMU)
add support for different kind of GPS (INS&RTK&cheap gps)
read GPS noise(and other sensors' noise) from config files
add support for different kind of IMU and ODOM
add support for more kind of pointclouds
add a Pose3d struct for simple operation of matrix4f or just use gtsam::pose3d
using Eigen::Vector instead of sensors::Vector3
use double instead of float (eigen::matrix)
using kdtree for loop detection (m2dp search and matching)
mrvm output to a special format data file and can be transformed to pcd independently
mrvm output center of voxels
add more config files and .sh files for other sensor-sets
using ecef or some other coordinate system instead of utm
gravity alignment (in pose extrapolator)

yowlings / StaticMapping