9.cartographer mapping algorithm

Cartographer： https://google-cartographer.readthedocs.io/en/latest/

Cartographer ROS： https://google-cartographer-ros.readthedocs.io/en/latest/

map_server： https://wiki.ros.org/map_server

9.1 Introduction

Cartographer is a 2D and 3D SLAM(simultaneous localization and mapping) library supported by Google's open source ROS system. Graphing algorithm based on graph optimization(multi-threaded backend optimization, problem optimization built by cere). Data from multiple sensors, such as LIDAR, IMU, and cameras, can be combined to simultaneously calculate the sensor's position and map the environment around the sensor.

The source code of cartographer mainly includes three parts: cartographer, cartographer_ros and ceres-solver(back-end optimization). cartographer

Cartographer uses the mainstream SLAM framework, which is a three-stage method of feature extraction, closed-loop detection, and back-end optimization. A submap submap is composed of a certain number of LaserScans, and a series of submap submaps constitute the global map. The short-term process of building submaps with LaserScan has little cumulative error, but the long-term process of building global maps with submaps will have large cumulative errors, so it is necessary to use closed-loop detection to correct the positions of these submaps. The basic unit of closed-loop detection is Submap, closed loop detection adopts scan_match strategy. The focus of cartographer is the creation of submap submaps that fuse multi-sensor data(odometry, IMU, LaserScan, etc.) and the implementation of the scan_match strategy for closed-loop detection.

• cartographer_ros

The cartographer_ros package runs under ROS. It can receive various sensor data in the form of ROS messages, and publish it in the form of messages after processing, which is convenient for debugging and visualization.

9.2 Use

Note: When building a map, the slower the speed, the better the effect(note that if the rotation speed is slower), the effect will be poor if the speed is too fast.

According to different models, you only need to set the purchased model in [.bashrc], X1(ordinary four-wheel drive) X3(Mike wheel) X3plus(Mike wheel mechanical arm) R2(Ackerman differential) and so on. Section takes X3 as an example

Open the [.bashrc] file

sudo vim .bashrc 

Find the [ROBOT_TYPE] parameter and modify the corresponding model

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export  ROBOT_TYPE=X3    # ROBOT_TYPE: X1 X3 X3plus R2 X7 

To replace the configuration file, copy the launch file and lua file required by cartographer to the specified directory. It has been configured before leaving the factory.

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sudo bash ~/yahboomcar_ws/src/yahboomcar_nav/scripts/copy_carto.sh 

9.2.1 Start

Start command(robot side), for the convenience of operation, this section takes [mono + laser + yahboomcar] as an example.

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roslaunch  yahboomcar_nav  laser_bringup.launch            # laser + yahboomcar 
roslaunch  yahboomcar_nav  laser_usb_bringup.launch        # mono + laser + yahboomcar 
roslaunch  yahboomcar_nav  laser_astrapro_bringup.launch   # Astra + laser + yahboomcar 

Mapping command(robot side)

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roslaunch  yahboomcar_nav  yahboomcar_map.launch  use_rviz:=false  map_type:=cartographer 

• [use_rviz] parameter: whether to enable rviz visualization.
• [map_type] parameter: set the mapping algorithm [cartographer].

Open the visual interface(virtual machine side)

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roslaunch yahboomcar_nav view_cartographer.launch 

The gap at the back of the robot is due to the occlusion of the installation position of the display screen, so a certain range of Lidar data is shielded. The shielding range can be adjusted or not shielded according to the actual situation. For details, see [01. Lidar Basic Course].

9.2.2 Control the robot

• The keyboard controls the movement of the robot

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rosrun teleop_twist_keyboard teleop_twist_keyboard.py   # system integration 
roslaunch yahboomcar_ctrl yahboom_keyboard.launch       # custom 

• The handle controls the movement of the robot

Make the robot walk all over the area to be built, and the map is as closed as possible.

There may be some scattered points during the mapping process. If the mapping environment is well closed and regular, the movement is slow, and the scattering phenomenon is much smaller.

9.2.3 Map save

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bash ~/yahboomcar_ws/src/yahboomcar_nav/maps/carto_map.sh                            

The map will be saved to ~/yahboomcar_ws/src/yahboomcar_nav/maps/ folder, a pgm image, a yaml file.

map.yaml

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image :  map.pgm 
resolution :  0.05   
origin :  [-15.4,-12.2,0.0]  
negate :  0 
occupied_thresh :  0.65 
free_thresh：  0.196 

Parameter parsing:

• image: The path of the map file, which can be an absolute path or a relative path
• resolution: the resolution of the map, m/pixel
• origin: The 2D pose(x, y, yaw) of the lower left corner of the map, where yaw is rotated counterclockwise(yaw=0 means no rotation). Many parts of the system currently ignore the yaw value.
• negate: whether to reverse the meaning of white/black, free/occupy(the interpretation of the threshold is not affected)
• occupied_thresh: Pixels with an occupancy probability greater than this threshold will be considered fully occupied.
• free_thresh: Pixels with an occupancy probability less than this threshold will be considered completely free.

9.2.4 Node View

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rqt_graph 

9.2.5 View tf tree

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rosrun rqt_tf_tree rqt_tf_tree 

9.3 configuration parameters

lua file