ŐŝůĞy ZŽďŽƚKŝƉĐĞƐŶ ^ƵŽƚƵŽƌƐǁĂĞ ƌ <Ğ ŝ ƵƚƚŽŶŽŵ
ŐŝůĞy ZŽďŽƚKŝƉĐĞƐŶ ^ƵŽƚƵŽƌƐǁĂĞ ƌ <Ğ ŝ ƵƚƚŽŶŽŵ
1. ĂƐŝĐ ŚĂƌĚǁĂƌĞ ĐŽŶĨŝŐƵƌĂƚŝŽŶ ů
Ž
a) ŽŵƉƵƚŝŶŐ ƵŶŝƚ ĂŶĚ ĂĐĐĞƐƐŽƌŝĞƐ
EŽ͘ | ĐĐĞƐƐ | DŽĚĞů | YƵĂŶƚŝ |
ϭ | ŽŵƉƵƚŝŶ | ^h^ ;sŝͲ ϳ ϵϳϬϬ ϭϲ' D͘Ϯ EsD | ϭ |
Ϯ | ŽŵƉƵƚŝŶ ĂĚĂƉƚĞ | Ϯϰǀ ƚŽ ϭ | ϭ |
ϯ | ƐĞƚ ŽĨ ŬĞLJďŽĂ | ϭ | |
ϰ | ϭϰͲŝŶĐŚ ǁ ƐĐƌĞĞŶ |
Ž
b) WĞƌĐĞƉƚŝŽŶ ĞƋƵŝƉŵĞŶƚ ĂŶĚ ĂĐĐĞƐƐŽƌŝĞ
EŽ͘ | ĐĐĞƐƐ | DŽĚĞů | YƵĂŶƚŝ |
ϭ | DƵůƚŝ ů | ZŽďŽƐĞŶƐ | ϭ |
Ϯ | Ϯϰs sZ | Ϯϰǀ ƚŽ ϭ |
Ž
c) /ŶƚĞŐƌĂƚĞĚ ŶĂǀŝŐĂƚŝŽŶ ĂŶĚ ĂĐĐĞƐƐŽƌŝ
EŽ͘ | ĐĐĞƐƐ | DŽĚĞů | YƵĂŶƚŝ |
ϭ | /ŶƚĞŐƌĂ ŶĂǀŝŐĂƚ | EĞǁƚŽŶ | ϭ |
Ϯ | Z& ĐŽŶŶ | Ϯ | |
ϯ | ĂƚĂͬWŽ ĐŽŶŶĞĐ | ϭ | |
ϰ | 'W^ ĂĞ | Ϯ | |
ϱ | ĂƐĞ ŽĨ | Ϯ |
Ž
d) ŚĂƐƐŝƐ ƉůĂƚĨŽƌŵ
EŽ͘ | ĐĐĞƐƐ | DŽĚĞů | YƵĂŶƚŝ |
ϭ | ŚĂƐƐŝƐ | ,hEdͬ, hZEd | ϭ |
ƉůĂƚĨŽ | |||
Ϯ | ZĞŵŽ ƚŽĞŶ ƚůƌĞ | &^ ŝϲƐ | ϭ |
ϯ | h^ ƚŽ | E ĂŶĂ | ϭ |
EŽƚĞ͗ ƵĞ ƚŽ ƚŚĞ ĚŝĨĨĞƌĞŶƚ ŶĞƚǁŽƌŬ ƐƚĂŶĚĂƌĚ ŝŶ Ě ƚŚĞ ƌŽƵƚĞƌ ƉŝƌƐŽ ǀŶŝŽĚƚĞ ǁďŝůƚĞŚ ŝƚŶŽ ƚŚĞ ĂĐĐĞƐƐŽƌŝĞƐ ůŝƐƚ͕ Ƶ ďĂƐĞĚ ŽŶ ƚŚĞ ƌĞƋƵŝƌĞŵĞŶƚ ďLJ ƚŚĞŵƐĞůǀĞƐ͘
2. ĂƐŝĐ ĨƵŶĐƚŝŽŶ ŽĨ ƵƚŽǁĂƌĞ ĚĞǀ ŽĨ K
1) ĂƐŝĐ ĨƵŶĐƚŝŽŶ ŽĨ ĚĞǀĞůŽƉŵĞŶƚ Ŭŝƚ
z /ŶƚƌŽĚƵĐƚŝŽŶ ƚŽ ĐƚŚĂĞƐ ƐǁŝŝƐƌĞ ĐŽŶƚƌŽů ŽĨ ƚŚĞ
z ŽŶƚƌŽů ƚŚĞ ĐŚĂƐƐŝƐ ďLJ ZK^
z sŝĞǁ ƚŚĞ ůŝĚĂƌ ϯ ƉŽŝŶƚ ĐůŽƵĚ ĚĂƚĂ ďĂƐĞĚ z hƐĞ ƵƚŽǁĂƌĞ ƚŽ ďƵŝůĚ ϯ ƉŽŝŶƚ ĐůŽƵĚ ŵĂƉ z hƐĞ ƵƚŽǁĂƌĞ ƚŽ ƌĞĐŽƌĚ ƉĂƚŚ ƉŽŝŶƚƐ
z hƐĞ ƵƚŽǁĂƌĞ ƚŽ ĨŽůůŽǁ ƉĂƚŚ ƉŽŝŶƚƐ
z hƐĞ ƵƚŽǁĂƌĞƉ ŽƚŝŽŶ ƚĨƐŽ ů;ůŽďǁƐ ƚƉĂĐƚůŚĞ ĂǀŽŝĚĂŶĐĞͿ z hƐĞ ŚLJďƌŝĚ Ύ ĨŽƌ ĨƌĞĞ ŶĂǀŝŐĂƚŝŽŶ ;ƐƚĂƚŝ z hƐĞ ƵƚŽǁĂƌĞ ĨŽƌ ůŽĐĂů ƉĂƌƚ ƉĂƚŚ ƉůĂŶŶŝŶ z Ěŝƚ ǀĞĐƚŽƌ ŵĂƉƐ ;ůĂŶĞ ůŝŶĞƐ͕ njĞďƌĂ ĐƌŽƐ z hƐ ĞƵ ƚŽ ǁĨĂŽƌĞ ŐůŽďĂů ƉĂŶƚĞŚĚ ƉǁůŝĂƚŶŚŶ ŝǀŶĞŐĐ ƚ;ŽĐƌŽ ŵŵďĂŝƉͿ
2) ĞƐĐƌŝƉƚŝŽŶ ŽĨ K
/ƚĞŵ | ŽŶƚĞŶƚ |
ƉƉůŝĐĂƚŝŽŶ | /ŶĚŽŽƌ ĂŶĚ ŽƵƚĚŽŽƌ ĞŶǀŝ |
ƉƉůŝĐĂďůĞ ƌ | ůĞĂƌ ZŽĂĚƐ ĂŶĚ ůŝŵŝƚĞĚ ĂƵƚŽŶŽŵŽƵƐ ĚƌŝǀŝŶŐͿ |
tĞĂƚŚĞƌ | ZĞŐƵůĂƌ ǁĞĂƚŚĐĞůƌŽ ƵƐĚƵLJĐ͕Ś ĂŶƐ ;ǀŝƐŝďŝůŝƚLJ ĂďŽǀĞ ϭϬϬ ŵ |
WĂǀĞŵĞŶƚ ƌĞƋ | ϭ͘ ^ŵŽŽƚŚ ĐĂůŶĞĚĂ Ŷƌ ĞƌůŽĂĂƚĚŝƐǀ Ğ;Ă ĐĞŵĞŶƚ ƌŽĂĚƐ͕ ĞƚĐ͘Ϳ͕ Ğdž ŵĂŶLJ ƉƌŽƚƌƵƐŝŽŶƐ Žƌ ĚĞƉ ƐĐĂƚƚĞƌĞĚ ŽďũĞĐƚƐ͖ Ϯ͘ ĞtŶǀĞŝƚƌ ŽŶ ;ŵƚĞŚŶĞƚ ĚǁĞĂƉƚĞŚƌ ŽŶƐĨŽŚ ƚŽ ĞdžĐĞĞĚ ƚŚĞ ŚĞ ŝďŐŽŚƚƚ ŽϱŽŵĐĨ ŵ Ĩ͕ƚŽ Śƌ ƉƵƚƚŝŶǁŐĂ ƚŝĞŶƌ ƚͿŚ͖Ğ ϯ͘ >ĞƐϭƐϬ Ɛ ΣůƚŽŚƉĂĞŶ ŝƐ ƌĞĐŽŵŵĞ ƵƉ ;ŝ ĐƚĂŶ ďĞ ŝŶĐƌĞĂƐĞĚ ĂƉƉƌ ĚŝĨĨĐĞŚƌĂĞƐŶƐƚŝ Ɛ ĚLJƌͿ ŝ͖ǀĞ ĐĂƉĂĐ |
sĂůŝĚ ƉĞƌŝŽĚ | dŚĞ ƉǁĞŚƌŝŝƚĐŽŚƌĚĞĞ ŝƐ ĞŶŽƵĚŐƵŚƌ ŝƐŶ ĚĂLJ ŚĂŝŶŐĚŚ ǀŝƐŝďŝůŝƚLJ Ăƚ Ŷ |
^ƉĞĞĚ | чϭϬŬŵͬŚ |
tŽƌŬŝŶŐ ŚƵŵŝ | ϬΕϴϬй |
3. ĂƐŝĐ /ŶƚƌŽĚƵĐƚŝŽŶ͗
i. ,ĂƌĚǁĂƌĞ /ŶƚƌŽĚƵĐƚŝŽŶ
1) ŚĂƐƐŝƐ ƉůĂƚĨŽƌŵ
HUNTER1.0
HUNTER1.0 is a programmable UGV with Xxxxxxxxx steering model which its chassis is based on Xxxxxxxxx steering theory. Therefore, it is similar to normal cars and high performances on cement and asphalt roads . Compared with the four- wheel differential chassis, HUNTER chassis has higher performances for load carrying and speed, also it causes less abrasion for the structure and tires. Although HUNTER is not designed for all kinds of terrains, it is equipped with a swing arm suspension which is able to go through some normal obstacles such as speed bumps, etc.
Additional extension such as stereo camera, laser lidar, GPS, IMU and robotic manipulator can be installed on HUNTER optionally. XXXXXX is mostly used for autonomous driving education ,research, indoor and outdoor security patrolling, environment sensing, general logistics and transportation.
HUNTER 2.0
HUNTER2.0 was born for low - speed self - driving which based on front - wheel Xxxxxxxx steering theory and swing arm suspension, is able to pass different kind
of obstacles, secondary development interfaces and standard installation
components are making HUNTER2.0 the best solution for mobile robot self - driving program. Compared with HUNTER1.0, the upgraded version HUNTER2.0
has gradient parking function which a chieved long - term ramp parking. If the vehicle is powered off or malfunctions while driving on a sloped road, the tires will
be locked, making it stable and reliable. HUNTER2.0 has lithium iron phosphate battery and the capacities can be customized based o n the requirement. The speed also can be customized up to 10km/h, meet the requirements of different autonomous driving scenarios.
2) Robosense Introduction
RS- LiDAR- 16 uses 16 laser heads to simultaneously emit high - frequency laser beams to cont inuously scan the external environment. Beca use it has high - speed digital signal processing technology and ranging algorithms to acquire three - dimensional space point cloud data and object reflectivity rate, so that the
machine is able to observe the surro unding and highly capable for location navigation and obstacles avoidance.
Figure 2 Robosense lidar
z TOF method ranging 16 channel
z Range: 20cm - 150m (Target reflectivity rate 20%)
Sensor
z z z z z | Precision:+/ - 2cm (Typical value) Visual angle (Vertical): 1r5 qTotgal 30 ) q Angular resolution: (Vertical): 2 q Visual angle (Horizontal) : 360 q Angular resolution (Horizontal/azimuth): 0.09 (q5Hz) to 2g0Hz) Speed: 300/600/1200rpm (5/10/20Hz) | 0.36 | q | |
z | ||||
Laser | z z z | Class 1 Wavelength: 905nm Laser launch angle: Horizontal 3mrad, Vertica l 1.2mrad | ||
Output | z z z | 320kBytes/s 100M Internet UDP include Distance information Rotation angle information Calibrated reflectivity information Synchronized time label (Resolution ratio 1us) | ||
Mechanical/electronic | z | Power consumption: 9w (Typical value) | ||
operation | z | Operational voltage: 12VDC (With interface box, stable voltage) | ||
z | Size: Diameter 109mm * Height 82.7mm | |||
z | Protective safety level: IP67 | |||
z | Operational temperature range: - 10 Cq~+60 Cq | |||
3) Introduction of c omputing unit
The computing unit uses intel i7 - 9700 processor which main frequency is eight - core and eight - wire 3Ghz , up to 32 GB memory and two hard disks.
ii. ^LJƐƚĞŵ ƌĐŚŝƚĞĐƚƵƌĞ
1) Introduction of Autoware system
Autoware is the first open source integration software for autonomous driving vehicle in the world. Autoware is mainly suitable for cities, but also applicable to highway and non - municipal roads . At the same time, there are development and
application resources on the Autoware open source software wh ich is built on ROS
operating system . The first official version was release d by the Nagoya University research team with the leadership of Xxxx. Xxxxxxx Xxxx in August 2015. In late December 2015, in order to maintain Autoware and apply it to real self - dr xxxxx cars , Xxxx. Xxxxxxx Xxxx founded Tier IV . As the time goes on, Autoware has
bec ame an open source project acknowledged by the public . Autoware is also the first "all in one " open source software for autonomous driving technology in
the world.
Autow are contains the required function modules. In this manual, there are only general concept for function modules, customers are welcome to develop detail research by their own.
Perception
Autoware support camera, LiDAR, IMU and GPS as the main sensor. From the technical view , if it is not verified, as long as the sensor driver software is provided,
almost all kinds of cameras, LiDAR, IMU and GPS can be applicable in Autoware.
Computing/Perception
The perception ability of Autoware is consist ed of local ization , perception and prediction. Though combining 3D maps with SLAM algorithms of GNSS and IMU sensor to achieve localization . Perception contains sensor fusion algorithm and deep neural networks camera and Lidar . Prediction is based on the results of localization and perception.
Localization
lidar_localizer use scan data from LiDAR and pre - install 3D map information to calculate self - driving car position in global coordinate (x, y, z, roll, pitch, yaw). We suggest to us e the NDT algorithm to match the L iDAR scan with the 3D map, and the ICP algorithm is also applicable .
gnss_localizer converts the NMEA messages from GNSS receiver to the (x, y, z, roll, pitch, yaw) position. This result can be used as the location of the autonomous
vehicle independently , or it can be used to initialize and supplement of the lidar_localizer result .
Generally, dead_reckoner uses IMU sensors to predict the next frame position of the autonomous vehicle, and interpolates the results of lidar_localizer and gnss_localizer.
Per ception
Lidar_detector acquires the point cloud data from 3D laser scanner and it has
object detect ing function which based on LiDAR. The Euclidean clustering algorithm supports the basic performance which is able to find the clusters of LiDAR scans (point clouds) above the ground. In order to classify clusters, it
support the algorithm base on DNN such as VoxelNet and LMNet.
vision_detector acquires image data from the camera and it has object detection function which is bas e d on image. Main algorithm include s R- CNN, SSD and Yolo which are designed to single DNN executing to achieve actual - time performance . and support various different detection types, such as cars and passengers.
vision_tracker is able to track the results of vision_detector. This algorithm is based on Beyond Pixels. Project the tracking result on the image platform, and combine
it with the result of lidar_detector in 3D space by using Fusion_tools. fusion_detector requires the point cloud data from laser sc anner and image data from camera, and achieve accurate target detection in 3D space. The position of
laser scanner and camera must be calibrated in advance. The current implementation is based on the MV3D algorithm, th is network has less extensibility comp ared with the original algorithm.
fusion_tools are able to combine the result of lidar_detector and vision_tracker. The information identified by vision_detector is add to the point cloud cluster detected by lidar_detector.
object_tracker is the motion of the object detected and recognized by the above procedure. The tracking results can be used for object behavior prediction in the fut ure and object velocity evaluation. The tracking algorithm is based on a Xxxxxx
filter. Another variant also supports parti cle filters.
Prediction
object_predictor uses the results of the above object tracking to predict the future route of moving objects (such as cars and passengers).
collision_predictor use the results of object_predictor to predict whether the
autonomous car is going to collide with any kinds of object in motion. In addition to the results of object tracking, the information of route trajectory and speed of
the autono mous vehicle is also required as input data.
cutin_predictor use the same information as collision_predictor did to predict whether there is any neighbour car cut in front of the autonomous vehicle. x
Computing/Decision
Autoware’s decision - making module co ntains perception and planning modules. According to the result of perception, the driving behavior of Autoware is represented by the finite state machine , so that the appropriate planning function
can be selected. The current decision - making method is bas ed on the rule system.
Computing/Planning
The last module of Autoware is the planning module which function is mak ing plans for global tasks and local (at the time) movement based on the results of the
perception and decision - making modules. Generally, th e global task is determined
when the autonomous vehicle is started or restarted, and the local motion is
updated based on the state xxxxx xxx . For example, if the state of Autoware is set to "Stop", the plan is set ting the speed of the autonomous vehicle to zero in front of an object with a safety margin or stop at the stop line. Another example is that
if the state of the automatic software is set to "Avoid", the trajectory of the
autonomous vehicle is planned to pass the obstacle. The main software package s included the planning module as follows.
Planning
x X U [ sZearcKhes Efor tVhe gRlobaGl rouTte toT theKdesXtination. The route is
represented by a set of intersections in the network.
lanxe_planner determines to use which lanes and the route generated by
route_planner. The lane is represented by a set of road signs and multiple road signs (each road sign is correspond ing to a lane) generated by this package.
x ] G _ V Ucan Obe uTsed Zto geEnerVate aRset Gof T T guKide pXoints to the
destination. The difference between this package and lane_planner is that it generates a single way point instead of an array of way point s.
x ] G _ V isUa prOacticTal toolZ fEor sSavinGg anQd loaKdinXg manual way points. If it
is needed to save way points to a specific file, please dr xxx the vehicle manually after activating localization, Autoware will record the way points and speed
information of the driving route. You can downl oad the recorded way point s from the specifi c file later, so that the motion planning module is able to fol low the
path.
Motion
x \ K R U geIt uOpdateZs fr_om ElaneV_plaRnnerG, waTypoiTnts_KplanXner or waypoints_maker
Speed plans for way points is slow/accelerate vehicles for different road
circumstances, such as stop lines and traffic lights. Please note that the speed information embedded in a given waypoint is static, and the package will update
the speed plan based on the driving circumstances.
astar_planner executes the hybrid A* search algorithm, this algorithm generates
the path from current position to spe cific position. The software package can be used to avoid obstacles and make sudden turns on given way points as well as
route selection in free spaces such as parking lots.
adas_lattice_planner execute the state lattices planning algorithm. The algorithm is based on a spline curve, a predefined parameter table and ADAS mapping (also
known as vector mapping) information generates multiple feasible trajectories before the current position. The software package is used for obstacle avoidance and lane changing .
waypoint_follower executes the Pure Pursuit algorithm. The algorithm generates a set of twisted velocities and angular velocities (or positive angles) to move the autonomous vehicle to a target waypoint on a given waypoint in circular motion. This packag e should be used in combination with velocity_planner, astar_planner
and/or adas_lattice_planner. The released set of twisted speed and angular speed (or only angle) information will be acquired by the vehicle controller or wire control interface. Finally, the autonomous vehicle will be under controlled automatically.
2) Autoware low speed autonomous driving kit structure
&ŝŐƵƌĞ ϱ sĞŚŝĐůĞ ƉůĂƚĨŽƌŵ ĐŚĂƐƐŝƐ ƐLJƐƚĞŵ
iii. ĂƐŝĐ ŝŶƚƌŽĚƵĐƚŝŽŶ ŽĨ ƐŽĨƚǁĂƌĞ
1) ĂƐŝĐ ŝŶƚƌŽĚƵĐƚŝŽŶ ŽĨ ZK^
ZK ^ ŝƐ KƉĞŶ ^ŽƵƌĐĞ ƌŽďŽƚ ŽƉĞƌĂƚŝŶŐ ƐLJƐƚĞŵ ŚĂƐ ŚŝŐŚůLJ ĨůĞdžŝďůĞ ƐŽĨƚǁĂƌĞ ĂƌĐŚŝƚĞĐƚƵƌĞ ƐƚƌƵĐƚƵƌĞ ĐŽŶŶĞĐƚƐ ĞĂĐŚ ŶŽĚĞ ;ŝŶĚĞƉĞŶĚĞŶƚ
ĐŽŵŵƵŶŝĐĂƚĞƐͰ /ďWĂ͕ĂŶ ƐĚĞ Ěƚ ŚŽĞŶ ŶdŽ ĚWĞƐ ĂƌĞ ĐŽŶŶĞĐƚĞĚ ǁ ƚŚƌŽƵŐŚ ĚŝĨĨĞƌĞŶƚ ƚŚĞŵĞƐ͘ dŚŝƐ ƐƚƌƵĐƚƵƌĞ ŝ ĐŽĚĞ ďĂƐĞ ĂŶĚ ƉƌŽƚŽĐŽů ǁŚŝĐŚ ŝƐ ĂŝŵĞĚ ƚŽ Ɛ ƚŚĞ ƉƌŽĐĞƐƐ ŽĨ ĐƌĞ ƌĂŽƚďŝŽŶƚŐ ďĐĞŽŚŵĂƉǀůŝĞŽdžƌ ƐĂ ŶŽĚŶ ƌƚŽŚďĞƵ Ɛƌƚ ŚĞůƉƐ ĂǀŽͲĐŝƌĚĞŝĂŶƚŐŝ ŶƚŐŚ Ğǁ ŚƌĞĞĞůƐ ƉƌŽďůĞŵ ƚŚĞ ǁŚŝĐŚ ĞĂƐŝĞƌ ĂŶĚ ĨĂƐƚĞƌ͕ ƐŬŝƉ ƚŚĞ ƌĞƉĞƚŝƚŝǀĞ ǁŽƌ
ZK ^ ŝƐ ĂŶ ŽƉĞŶ ƐŽƵƌĐĞ ŽƉĞƌĂƚŝŶŐ ƐLJƐƚĞŵ ĨŽ ŽƉĞŝƌŶĂŐƚ ƐLJƐƚĞŵ ĚŽ͕ ŝŶĐůƵĚͲůŝĞŶǀŐĞ ůŚ ĂĚƌĞĚǀǁŝĂĐƌĞ ĐĂŽďŶƐƚƌŽ ŝŵƉůĞŵĞŶƚĂƚŝŽŶ ŽĨ ĐŽŵŵŽŶͲƉůĂLJƐ ƐƵŝƐŶĞŐĚ ďĨĞƵƚŶǁĐĞƚĞŝŶŽ ŶĂů ƉƌŽĐĞƐƐĞƐ͕ ĂŶĚ ƉĂĐŬĂŐĞ ŵĂŶĂŐĞŵĞŶƚ͘ /ƚ ĂůƐŽ ĨƵŶĐƚŝŽŶ ĨŽƌ ĂĐƚƋƌƵĂŝŶƌƐŝůŶĂŐƚ͕ŝ ŶĞŐĚ͕ŝ ƚĐŝŽŶŵŐƉ ůĂLJŶŝĚŶ Ő ĂŶĚ ƌ ĐŽŵƉƵƚĞƌƐ͘ ƚ ƚŚĞ ƐĂŵĞ ƚŝŵĞ͕ ͲƉZĂKƌ^ƚ LJŝ Ɛů ŝĂďůƌƐĂŽƌ ŝĐĞ ŝŶĐůƵĚŝŶŐ ŽƉĞŶĐǀ ;ĐŽŵƉƵƚĞƌ ǀŝƐŝŽŶͿ͕ W > ;Ɖ ŽĨ ZK^ ŝƐ ĂůƐŽ ĐǀŽĞŵƌƉLJĂ ƚĚŝŝďǀůĞĞƌ ƐǁŝŝƚƚLJŚ ǁŵŚŽŝƐĐƚŚ ƐŝĞƐŶƐŽƌ ĂŶĚ ƵůƚƌĂƐŽŶŝĐ ŽŶ ƚŚĞ ŵĂƌŬĞƚ͘ hƐĞƌƐ ĐĂŶ ĂĚ ďĂƐĞĚ ŽŶ ƚŚĞŝƌ ƌĞƋƵŝƌĞŵĞŶƚ͘ ŶĚ ZK^ t/</ Ɖ ǁŚŝĐŚ ĚĞǀĞůŽƉĞĚ ďLJ ƵĂƐĐĞŬƌĂƐŐ Ğ͕Ɛ ƚĐŚĂ͛ƐĞŶ Ɛ ƉĞŵƌ ĞŽĞĨƉƚĞ ƐŶƵƐ ƐŝƐĞŽƌŶƵĂƌ ƉƌŽũĞĐƚ ƌĞƋƵŝƌĞŵĞŶƚƐ͘ dŚĞ ŵĂŝŶ ĨƵŶĐƚŝŽŶ ŽĨ ĐŽĚĞ ƌĞƵƐŝŶŐ ƐƵƉƉŽƌƚ ĨŽƌ ƌŽďŽƚ ƌĞƐĞĂƌĐŚŝŶŐ
ƉƌŽĐĞƐƐ ;ŶŽĚĞͿ ĨƌĂŵĞ͕ ƚŚƚĞŚƐĞĞ ƉƉƌƌŽŽĐĐĞĞĚƐƵƐƌ ĞĂƐƌ ĞƉ ĂĞĐŶŬ ĂŶĚ ĨƵŶĐƚŝŽŶ ƚŚĂƚ ĂƌĞ ĞĂƐLJ ƚŽ ƐŚĂƌĞ ĂŶĚ ƌĞ ƐŝŵŝůĂƌ ƚŽ ĐŽĚĞ ƌĞƉŽƐŝƚŽƌLJ ǁŚŝĐŚ ĐĂŶ ĂĐŚŝĞ ĚĞƐŝŐŶ ĐĂŶ ŵĂŬĞ ƚŚĞ ĚĞǀĞůŽƉŵĐĞŽŶŵƚƉ ůĂĞŶƚĚĞ ůŝLJŵ ƉůĞŵ ŝŶĚĞƉĞŶĚĞŶƚ ĨƌŽŵ ƚŚĞ ĨŝůĞ ƐLJƐƚĞŵ ƚŽ ƚŚĞ ƵƐ ƐĂŵĞ ƚŝŵĞ͕ Ăůů ƉƌŽũĞĐƚƐ ĐĂŶ ďĞ ŝŶƚĞŐƌĂƚĞĚ ƚŽ ŵĂŬĞ ƌŽďŽƚ ĚĞǀĞůŽƉŵĞŶƚ ďĞĐŽŵĞ ĞĂƐŝĞƌ͕ Ĩ
2) ĂƐŝĐ ŝŶƚƌŽĚƵĐƚŝŽŶ ŽĨ ƵƚŽǁĂƌĞ
Ƶ ƚŽǁĂƌĞ ŝƐ ƚŚĞ ĨŝƌƐƚ ŝŶƚĞŐƌĂƚŝŽŶ ŽƉĞŶ ƐŽ ǀĞŚŝĐůĞ ŝŶ ƚŚĞ ǁŽƌůĚ͘ DŽƐƚůLJ͕ ƵƚŽǁĂƌĞ ŝƐ ŝŶƚĞƌƐĞĐƚŝŽͲŶĨ ĞĂŶƌĐĞ ƐĂ ƌĂĞŶ ĚĂ ůŐƐĞŽŽ Ă ƉƵƉƚůŽŝǁĐĂĂƌďĞůΖĞƐ͘ Đ ŽůĚůĞ ďĂƐĞ ĂƌĞ ƌĞƐĞƌǀĞĚ ďLJ ŝƚƐLJ ƚƌŚƐĞĞ͕Ă Ɛ ǁŽƉĞŶĂ ĐƉŚƌĞŽ ǀϮŝ ĚůĞŝ ĐĂĞ Ŷ ƐŝŵƵůĂƚŝŽŶ ĞŶǀŝƌŽŶŵĞŶƚ ďĂƐĞĚ ŽͲŶĚ ƌZŝKǀ^ŝ Ŷ Ő' ĨŽƌ ƚĞĐŚŶŽůŽŐLJ͘
;ďͿ ^LJƐƚĞŵ ƐŽĨƚǁĂƌĞ ĂŶĚ ŚĂƌĚǁĂƌĞ
ϭ͘ ,Ă ƌŝĚŶǁƐĂƚƌĂĞůůĂƚŝŽŶ
ĂͿ ĐĐĞƐƐŽƌŝĞƐ ůŝƐƚ
ĐĐĞƐƐŽƌ | dĂďůĞ ŽĨ | YƵĂŶƚŝƚLJ ĐŽŵƉŽŶĞŶƚ | ZĞŵĂƌŬƐ |
ŽŵƉƵƚŝŶ | ŽŵƉƵƚŝŶŐ | ϭ | |
DŽƵƐĞ ĂŶĚ | ϭ | ||
DƵůƚŝ ůŝ | DƵůƚŝ ůŝŶĞ | ϭ | |
^ĞŶƐŽƌ ĐŽŶ | ϭ | ||
>ŝƋƵŝĚ Đ ĚŝƐƉůĂLJ | >ŝƋƵŝĚ ĐƌLJ ƐĐƌĞĞŶ | ϭ | |
ŵŝͲŶŚŝĚŵŝ ƚŽ | ϭ | ||
ƵƐď ƚͲĐŽ ǁƚŝLJƌƉ | ϭ | ||
ƵƐͲƚďͲŽĐĂŶ | ƵƐͲƚďͲŽĐĂŶ ŵŽĚ | ϭ | |
WŽǁĞƌ ŵŽ | Ϯϰǀ ƚŽ ϭϮǀ | ϭ | |
Ϯϰǀ ƚŽ ϭϵǀ | ϭ | ||
ŚĂƐƐŝƐ | ,hEd Z ŵŽď | ϭ | |
ǀŝĂƚŝ ;ŽǁŶŝ ƚƉŚ | ϭ | ||
sĞŚŝĐůĞ ĐŽ | ϭ |
EŽƚĞ͗ ƵĞ ƚŽ ƚŚĞ ĚŝĨĨĞƌĞŶƚ ŶĞƚǁŽƌŬ ƐƚĂŶĚĂƌĚ ŝŶ Ě ƚŚĞ ƌŽƵƚĞƌ ŝƐ ŶŽƚ ĂďůĞ ƚŽ ƉƌŽǀŝĚĞ ǁŝƚŚŝŶ ƚŚĞ ĂĐĐ ďĂƐĞĚ ŽŶ ƚŚĞ ƌĞƋƵŝƌĞŵĞŶƚ ďLJ ƚŚĞŵƐĞůǀĞƐ͘
ďͿ Ğ ƐĐƐĐŽƌŝĞƐ ĞůĞĐƚƌŝĐĂů ĚĞƐĐƌŝƉƚŝŽŶ
ĐĐĞƐƐŽƌŝĞƐ | ůĞĐƚƌŝĐĂů ĂĐĐĞƐƐŽƌŝĞƐ | ZĞŵĂƌŬƐ |
ŽŵƉƵƚŝŶŐ Ƶ | ϭϵǀΛϲ͘ϱĂ | |
DƵůƚŝ ůŝŶĞ | ϭ ϮǀΛϬ ;͘dϴLJĂƉŝĐ ǀĂůƵĞƐ ϵǁͿ | |
>ŝƋƵŝĚ ĐƌLJƐ ƐĐƌĞĞŶ | ϱǀ | |
ϰ'Z ŽƵƚĞƌ | ϭϮǀΛϬ͘ϴĂ |
ĐͿ WŽǁĞƌ ĐŽŶŶĞĐƚŝŽŶ ƚŽƉŽŐƌĂƉŚŝĐ ĚŝĂŐƌĂŵ dŚĞ ǀĞŚŝĐůĞ ĐŽŶƚĂŝŶƐ ƚǁŽ ǀŽůƚĂŐĞ ĐŽŶǀĞƌƐŝŽ ƚŚĞ ďĂƚƚĞƌLJ ŽĨ ƚŚĞ ĐŚĂƐƐŝƐ͕ ĂŶĚ ďŽƚŚ ĂƌĞ Ĩ ŚƵŶƚĞƌ͘ dŚĞ ǀŽϮůǀ͕ƚ ĂĂŐŶĞĚ ŝŝƐƚ Ϯǁϭŝ͘ůϱůǀ ΕĐϮŚϵĂ͘ŶŐĞ ǁŝƚŚ ƚ
ĚƵƌŝŶ͘Ő ,ƵŽƐǁĞĞ ǀƐĞŶ͕Ɛ ŽƚƌŚ ĞŵŽĚƵůĞ ŵĞŶƚŝŽŶĞĚ ĂďŽǀĞ ŵĂ ĞůĞĐƚƌŝĐĂů ĐŚĂƌĂĐƚĞƌŝƐƚŝĐƐ͕ ŽŶĞ ŝƐ ϭϮǀ ĂŶĚ ŵŽĚƵůĞƐ ĂƌĞĐ ŚƵĂ͘ƐĞƐĚŝ ƐŝŶ ƚŚĞ
ĚͿ ĨĂůƚŽĂǁ ĚŝĂŐƌĂŵ
dŚ Ğ ƵƚŽǁĂƌĞ ƵƚŽŶŽŵŽƵƐ ƌŝǀŝŶŐ ĚƵĐĂƚŝŽŶ ĨƌŽͲĞŶŶƚĚ ƉĞƌĐĞƉƚŝŽŶ͕ ƚŚĞ ŝŶƚĞƌŵĞĚŝĂƚĞ ĚĂƚĂ ƚƌ ĂŶĚ ƚͲŚĞĞŶ Ěƌ ĞĂĂĐƌƚƵĂƚͲĞŽŶƌĚ͘ ƉdĞŚƌĞĐ ĞĨƉƌƚŽŝŶŽƚŶ ŝƐ ĐŽŶƐŝƐƚ Ž ƐĞŶƐŽƌ ͲŐĂƉŶƐĚ ;ƌĂƚĚŬĂƉƚŝŶŐͿ͘ /Ŷ ŽƌĚĞƌ ƚŽ ĨĂĐŝůŝƚĂ ƐĞŶƐŽƌƐ͕ Ă ϰ' ƌŽƵƚĞƌ ŝƐ ŝŶĐůƵĚĞĚ ŝŶ ƚŚĞ ĂĐ ŽƚŚĞƌ ƐĞŶƐŽƌ ƵŶŝƚƐ͘ dŚĞ ĚĂƚĂ ƉƌŽĐĞƐƐŝŶŐ ƵŶ ƉƌŽĐĞĂƐŶƐĚŽ ƌŝ͕ƚ ŝƐ ĞƋƵŝƉƉĞĚ ǁŝƚŚ Ă ƐĐƌĞĞŶ ƚŽ ĚĞ ĚŝĂŐƌĂŵ ŝƐ ƐŚŽǁŶ ŝŶ ƚŚĞ ĨŽůůŽǁŝŶŐ ĨŝŐƵƌĞ͘
ĞͿ /ŶƐƚĂůůĂƚŝŽŶ
2. ^ŽĨƚǁĂƌĞ ŝŶƐƚĂůůĂƚŝŽŶ
ZK^ /ŶƐƚĂůůŚĂƚƚŝƉŽ͗Ŷ͕ͬͬ ǁƌŝĞŬĨŝĞ͘ƌƌ ŽƚƐŽ͗͘Ž ƌŐͬŬŝŶĞƚŝĐͬ/ŶƐƚ
$ sudo sh - c ’. /etc/lsb - release && echo "deb xxxx://xxxxxxx.xxxx.xxx.xx/xxx/xxxxxx/ ‘lsb_release - cs‘ main" >
/etc/apt/sources.list.d/ros - latest.list ’
$ sudo apt - key adv -- keyserver ’hkp://xxxxxxxxx.xxxxxx.xxx:80’ -- recv - key C1CF6E31E6BADE8868B172B4F42ED6FBAB17C654
$ sudo apt - get update
$ sudo apt - get install ros - kinetic - desktop - full
$ apt - cache search ros - kinetic
$ echo "source /opt/ros/kinetic/setup.bash" >> ~/.bashrc
$ source | ~/.bashrc | |||
$ sudo apt | install python | - rosdep python | - rosinstall python | - |
rosinstall | - generator python | - wstool build | - essential | |
$ sudo apt install python - rosdep
$ sudo rosdep init
EŽƌŵĂůůLJ͕ ƚŚĞƌĞ ǁŽƵůĚ ďĞ ĂŶ ĞƌƌŽƌ͕ƚ Žƚ͗Ś ĞŶ ŝƚ ŚƚƚƉƐ͗ͬͬďůŽŐ͘ĐƐĚŶ͘ŶĞƚͬƵϬϭϯϰϲϴϲϭϰͬĂƌƚŝĐůĞͬĚ ηKƉĞŶ ŚŽƐƚƐ ĨŝůĞ
sudo gedit /etc/hosts
η ĚĚ ƚŽ ƚŚĞ ĞŶĚ ŽĨ ƚŚĞ ĨŝůĞ ϭϱϭ͘ϭϬϭ͘ƌϴĂϰǁ͘ŐϭŝϯƚϯŚ ƵďƵƐĞƌĐŽŶƚĞŶƚ͘ĐŽŵ
η džĂŝĨƚ Ğƌ ƐĂǀŝŶŐ ĂŶĚ ƚŚĞŶ ƚƌLJ ĂŐĂŝŶ
$ sudo rosdep init
$ rosdep update
ƌĞĂƚĞ ǁ͗ŽƌŬƐƉĂĐĞ
$ mkdir - p ~/catkin_ws/src
$ cd catkin_ws/src/
$ catkin_init_workspace
$ cd ..
$ catkin_make
$ echo "source ~/catkin_ws/devel/setup.bash" >> ~/.bashrc
$ source ~/.bashrc
ZĞƐƚĂƌƚ ƚŚĞ ĐŽŵƉƵƚĞƌ͘
/ŶƐƚĂů ůĂĂŶƚ ŝĂŽŶŶƌĂ Ğů͗ŽůLJĨLJnj Ğƌ
ŽƉlibLJco xxxxxxxx.xx to /usr/local/lib
$ sudo cp xxxxxxxxxxxxx.xx /usr/local/lib Can authorization Configuration y
$ sudo gedit /etc/udev/rules.d/99 - mysub.rules
Add contents:
##
ACTION=="add",SU BSYSTEMS==
"usb",ATTRS{idVendor}=="04d8", ATTRS{idProduct}=="0053", GROUP="users",MODE="0777"
tŚĞŶ ƚŚĞ ĐŽŶĨŝŐƵƌĂƚŝŽŶ ŝƐ ĐŽŵƉůĞƚĞĚ
$ sudo ldconfig
ŽŵƉŝůĞ ƚŚĞ ĐŽĚĞ͗
ŽƉLJ ŚƵŶƚĞƌͺƌŽďŽƚ ŝŶ ƚŚĞ ƐƌĐ ĨŝůĞ ĨŽůĚĞƌ ƚ
$ cd ~/catkin_ws/
$ catkin_make
/ŶƐƚĂůů Ƌƚ͗ ; hƐĞ ϱ͘ϲ͘Ϯ ǀĞƌƐŝŽŶ ŚĞƌĞͿ Ƌƚ ĚŽǁŶů͗ŚŽ ƚĂƚĚƉ ƐƉ͗ĂͬŐͬĞǁǁǁ͘Ƌƚ͘ŝŽͬĚŽǁŶůŽĂĚ
'Ž ƚŽ ƚŚĞ ĚŝƌĞĐƚŽƌLJ ǁŝƚŚ ƚŚĞ Ƌƚ ŝŶƐƚĂůůĂƚŝ
$ sudo chmod +x qt - opensource - linux - x64 - 0.0.0.xxx
$ ./qt - opensource - linux - x64 - 0.0.0.xxx
<ĞĞƉ ĐůŝĐŬŝŶŐ EĞdžƚ ƵŶƚŝů ƚŚĞ ŝŶƐƚĂůůĂƚŝŽŶ
/ŶƐƚĂůů ŽƉĞŶĐǀ͗
/ŶƐƚĂůů ϯ͘ϰ͘Ϯ ǀŚĞƚƌƚƐƉŝƐŽ͗Ŷͬ ͬŚŽĞƉƌĞĞŶ͕Đ ǀŝ͘ŶŽƚƌĞŐƌͬůƌŝĞŶůŬĞ͗Ă ƐĞƐͬ ZĞĨĞŚƌƚ ƚƉŽƐ͗͗ ͬͬďůŽŐ͘ĐƐĚŶ͘ŶĞƚͬƵϬϭϬϲϯϮϭϲϱͬĂƌƚŝĐ
hŶnjŝƉ ŽƉĞŶĐǀ͕ ĂŶĚ ƚŚĞŶ ĞŶƚĞƌ ƚŚĞ ŽƉĞŶĐǀ ĨŽ
$ sudo mkdir build
$ cd build
$ cmake ../
$ make - j8
$ sudo make install
/ŶƐƚĂŽůǁůĂ ƌ ĞƵ͕ƚ ƵƐĞ ϭ͘ϴ͘Ϭ ǀĞƌƐŝŽŶ ŚĞƌĞ͕ ƐŽƵƌĐĞ ŚƚƚƉƐ͗ͬͬŐŝƚůĂď͘ĐŽŵͬ ƵƚŽǁĂƌĞͲͬĨƚŽƌƵĞŶĞĚͬĂϭƚ͘ŝϴŽ͘ŶϬͬ ƵƚŽ
/Ĩ ƚŚĞƌĞ ŝƐ ĂŶLJ ƉƌŽďůĞŵ ǁŝƚŚ ƚŚĞ ŝŶƐƚĂůůĂƚ ŚƚƚƉƐ͗ͬͬďůŽŐ͘ĐƐĚŶ͘ŶĞƚͬLJŽƵƌŐƌĞĂƚĨĂƚŚĞƌͬĂƌƚŝ
hŶnjŝƉ ƵƚŽǁĂƌĞ
$ cd Autoware - 1.8.0/ros/ One - click installation of all relies:
$ rosdep install - y -- from - paths src -- ignore - src -- rosdistro
$ROS_DISTRO
Compile
$ ./catkin_make_release
^ƚĂƌƚ ƵƚŽǁĂƌĞ͗
$ cd Autoware - 1.8.0/ros/
$ ./run
ĨƚĞƌ Ƶ Ɖ ƐƐƵƚĐĂĐƌĞůƚ͕ƐLJ ƐƐĨ ƵƚƵƉĂů ƌŝƚŶ ƚŝĞƐƌ ĨƐĂŚĐŽĞǁŶ͗ ĂƐ ĨŽůůŽǁ
3. sĞŚŝĐůĞ ǁŝƌĞ ĐŽŶƚƌŽů
tŝƚŚƚ ĞƚĐŚŚĞŶ ŽĚůĞŽǀŐĞŝůĐŽĂƉůŵ ĞŶƚ ŽĨ ĂƵƚŽŵŽƚŝǀĞ ĞůĞĐƚ ŝŶƚĞŐƌĂƚŝŽŶ ŽĨ ĂƵƚŽŵŽƚŝǀďĞLJ ƐŵLJĞƐĂĞƚŶůĞƐĞŵ ĐƐŽƚ͕Ĩƌ ŽƉŶĞŝŽĐƉ ůĞ ŝŶƐƚĞƚĂƌĚĂ ĚŽŝĨƚ ŝŽŶĂů ŵĞĐŚĂŶŝĐĂů ŵĞĐ dŚĂŝŶƐŝ ƐŵƐ ƚŽ ƚ ĞůĞĐƚƌŽŶŝĐ Ͳ ƚͲLJtĞŝĐƌŚĞŶͲ͘Žt ůŝΗŽƌ ĞŐLJΗLJ ĐŝĂƐŶ yďĞ ĐĂůůĞĚ ĞůĞĐƚƌ ĂŶĚ ΗyΗ ƌĞƉƌĞƐĞŶƚƐ ǀĂƌŝŽƵƐͲď ͲLJǁƐŝLJƌƐĞƚ͕ĞͲ ŵͲLJ Ɛƌ ĂŝŬŶĞ ƚŚĞ tŝƌĞ͕ ĞƚĐ͘
tŝƌĞ ĐŽŶƚƌŽůůŝŶŐ ŝƐ ƚŚĞ ďĂƐŝƐƚ ŽŽĨĨ ĂǁƵŝƚƌŽĞŵ Ăƚ ĐŽŶƚƌŽůůŝŶŐ ŝƐ ĐŚĂŶŐŝŶŐ Ăůů ƚŚĞ ĐŽŶƚƌŽů ďĞ ĞůĞĐƚƌŽŶŝĐ ĐŽŶƚƌŽů ͕ ĨƌŽŵ ƚŚĞ ŽƌŝŐŝŶĂů ĂŶĂ
ŐŝůĞdž ZŽďŽƚŝĐƐ ĐŚĂƐƐŝƐ ,hEd ZŚ ƌƉŽƌƚŽƚǀůŝĞĚ ĞďƐLJ ƐĞ
ǁŝƌĞ͕ ĂŶĚ ďƌĂŬĞ ďLJ ǁŝƌĞ͘ ĞƐŝĚĞƐ ƚŚĞ ďĂƐŝĐ ŝŶƚĞƌĨĂĐĞ ĂůƐŽ ƉƌŽǀŝĚĞƐ ƐŽŵĞ ĨĞĞĚďĂĐŬ ŝŶĨŽ dŚĞ E ĐŽŵŵƵŶŝĐĂƚŝŽŶ ƐƵƚƐĂĞƚŶƐŚĚ ĞĂ ƌ Ě EŝϮŶ͘ Ϭ, h Ed Z
ƐƚĂŶĚĂƌĚ͕ ďĂƵĞĚƐ ƐƌĂĂŐƚĞĞŝ DƐĨŝŽ ŽƐƚƌ ŽŵϱƌĂϬŽƚϬů <Ă d͕ Ś ĨĞŵŽ ƌůŵŝĂŶƚĞ͘Ăƌ ǀĞůŽĐ ĂŶĚ ƚŚĞ ĂŶŐƵůĂƌ ǀĞůŽĐŝƚLJ ŽĨ ƌŽƚĂƚŝŽŶ ŽĨ ƚŚ ĞdžƚĞƌŶĂů E ďƵƐ ŝŶƚĞƌĨĂĐĞ͘ dŚĞ ŝŶĨŽƌŵĂƚŝŽ
,hEd Z ĐŚĂƐƐŝƐ ƐƚĂƚƵ ƐZ ͘ǁ ŽƵůĚ ďĞ ŐŝǀĞŶ ďLJ ,h dŚĞ ƉƌŽƚŽĐŽů ŝŶĐůƵĚĞƐ ƐLJƐƚĞŵ ƐƚĂƚƵƐ ĨĞĞĚ ĨƌĂŵĞ͕ ĂŶĚ ĐŽŶƚƌŽů ĨƌĂŵĞ͘ dŚĞ ĐŽŶƚĞŶƚ ŽĨ ƚ dŚĞ ƐLJƐƚĞŵ ƐƚĂƚƵƐ ĨĞĞĚďĂĐŬ ĐŽŵŵĂŶĚ ŝŶĐůƵ
ĨĞĞĚďĂĐŬ͕ ĐŽŶƚƌŽŬů͕ ŵďŽĂĚƚĞƚ ĞƐƌƚLJĂ ƚǀƵŽƐů ƚĨĂĞŐĞĞĚ ďĨĂĞĐĞĚďĂĐ ĨĞĞĚďĂĐŬ͘ dŚĞ ƉƌŽƚŽĐŽů ĐŽŶƚĞŶƚ ŝƐ ƐŚŽǁŶ ŝŶ
ŽŵŵĂŶĚ EĂŵĞ
^LJƐƚĞŵ ^ƚĂƚƵƐ &ĞĞĚďĂĐ
^ĞŶĚŝŶŐ ZĞĐĞŝǀŝ /
LJĐůĞ;
ZĞĐĞͲƚŝŝǀŵĞ ŽƵƚ;ŵƐͿ
^ƚĞͲďĞͲLJǁƌŝƌ
ĞĐŝͲŵƐĂŝŬŽ
Ϭîϭϱϭ
ϮϬŵƐ
EŽŶĞ
ĐŚĂƐƐŝƐ ĐŽŶƚƌŽů
ĂƚĂ ůĞ ϬîϬϴ WŽƐŝƚŝŽ &ƵŶĐƚŝŽ
ƵƌƌĞŶƚ ďLJƚĞϬ ŽĨ ǀĞŚŝ
ďLJƚĞϭ DŽĚĞ ĐŽ ďLJƚĞϮ ĂƚƚĞƌLJ
ŚŝŐŚĞƌ ďLJƚĞϯ ĂƚƚĞƌLJ
ůŽǁĞƌ ϴ &ĂŝůƵƌĞ
ďLJƚĞϰ ŝŶĨŽƌŵĂ
ŚŝŐŚĞƌ &ĂŝůƵƌĞ
ďLJƚĞϱ ŝŶĨŽƌŵĂ
ůŽǁĞƌ ϴ
ŽƵŶƚ Ɖ ďLJƚĞϲ ;ĐŽƵŶƚͿ
ďLJƚĞϳ WĂƌŝƚLJ
;ĐŚĞĐŬƐ
ĂƚĂ ƚ
ƵŶƐŝŐŶ
ƵŶƐŝŝŐŶ
ƵŶƐŝŐŶ
ŝŶƚϭϲ
ƵŶƐŝŐŶ
ŝŶƚϭϲ
ƵŶƐŝŐŶ
ƵŶƐŝŐŶ
ĞƐĐƌŝƉƚŝŽŶ ϬîϬϬ ^LJƐƚĞŵ ŝŶ ĐŽŶĚŝƚŝŽŶ
ϬîϬϭ ŵĞƌŐĞŶĐLJ ŵŽĚĞ;ŶŽƚ ĞŶĂď ϬîϬϭ ^LJƐƚĞŵ Ğdž ϬîϬϬ ZĞŵŽƚĞ ĐŽ ϬîϬϭ ŽŵŵĂŶĚ Đ
ĐƚƵĂů ǀŽůƚĂŐ ĂĐĐƵƌĂĐLJ ŽĨ Ϭ
^ĞĞ ŶŽƚĞƐ ˇΎĨΎŽ—ƌ
ϬͲϮϱϱ ĐŽƵŶƚŝŶŐ ďĞ ĂĚĚĞĚ ǁŚŝů ĐŽŵŵĂŶĚ ŚĂƐ ď WĂƌďŝƚLJ
ĞƐĐƌŝƉƚŝŽŶ ŽĨ &ĂŝůƵƌĞ /Ŷ | ||
LJƚĞ | ŝƚ | DĞĂŶŝŶŐ |
ďLJƚĞ | ďŝƚ | ŚĞĐŬ ĞƌƌŽƌ ŽĨ E ĐŽŵŵƵŶ ĨĂŝůƵƌĞ ϭ͗ &ĂŝůƵƌĞͿ |
ďŝƚ | ďŶŽƌŵĂů ĐŽŶĚŝƚŝŽŶ ŽĨ ĨƌŽ ĨĂŝůƵƌĞ ϭ͗ &ĂŝůƵƌĞͿ | |
ďŝƚ | Z ƚ ƌĂŶƐŵŝƚƚĞƌ ĚŝƐĐŽŶŶĞĐƚŝŽ &ĂŝůƵƌĞͿϭ | |
ďŝƚ | ZĞƐĞƌǀĞĚ͕ ĚĞĨĂƵůƚ Ϭ | |
ďŝƚ | ZĞƐĞƌǀĞĚ͕ ĚĞĨĂƵůƚ Ϭ | |
ďŝƚ | ZĞƐĞƌǀĞĚ͕ ĚĞĨĂƵůƚ Ϭ | |
ďŝƚ | ZĞƐĞƌǀĞĚ͕ ĚĞĨĂƵůƚ Ϭ | |
ďŝƚ | ZĞƐĞƌǀĞĚ͕ ĚĞĨĂƵůƚ Ϭ | |
ďLJƚĞ | ďŝƚ | ĂƚƚĞƌͲǀLJŽ ůƵƚŶĂĚŐĞĞƌ ĨĂŝůƵƌĞ ;Ϭ͗ |
ďŝƚ | ĂƚƚĞƌͲǀLJŽ ůŽƚǀĂĞŐƌĞ ĨĂŝůƵƌĞ ;Ϭ͗ | |
ďŝƚ | EŽ͘ϭ ŵŽƚŽƌ ĐŽŵŵƵŶŝĐĂƚŝŽŶ | |
ďŝƚ | EŽ͘Ϯ ŵŽƚŽƌ ĐŽŵŵƵŶŝĐĂƚ&ŝĂŽŝŶů | |
ďŝƚ | EŽ͘ϯ ŵŽƚŽƌ ĐŽŵŵƵŶŝĐĂƚŝŽŶ | |
ďŝƚ | EŽ͘ϰ ŵŽƚŽƌ ĐŽŵŵƵŶŝĐĂƚŝŽŶ | |
ďŝƚ | DŽƚŽƌ ĚͲƌƚŝĞǀŵĞƉ ĞŽƌǀĂĞƚƌƵƌĞ ĨĂŝůƵƌ &ĂŝůƵƌĞͿ | |
ďŝƚ | DŽƚŽƌͲĐ ƵŽƌǀƌĞĞƌŶƚ ĨĂŝůƵƌĞ ;Ϭ͗ | |
dŚĞ ĐŽŵŵĂŶĚ ŽĨ ŵŽǀĞŵĞŶƚ ĐŽŶƚƌŽů ĨĞĞĚďĂĐŬ Ĩ ůŝŶĞĂƌ ƐƉĞĞĚ ĂŵŶŽĚǀ ŝĂŶŐ ƵǀůĞĂŚƌŝ ĐƐůƉĞ ĞďĚŽ ĚŽLJĨ͘ &Žƌ ƚŚĞ ƉƌŽƚŽĐŽů͕ ƉůĞĂƐĞ ƌĞĨĞƌ ƚŽ dĂďůĞ ϯ͘Ϯ͘
ŽŵŵĂŶĚ EĂŵĞ
DŽǀĞŵĞŶƚ ĐŽŶƚƌŽů &ĞĞĚď
EŽŶĞ
ϮϬŵƐ
Ϭîϭϯϭ
LJĐůĞ;
/
^ĞŶĚŝŶŐ ZĞĐĞŝǀŝ
ZĞĐĞͲƚŝŝǀŵĞ ŽƵƚ;ŵƐͿ
^ƚĞͲďĞͲLJǁƌŝƌ ĞĐŝͲŵƐĂŝŬŽ
ĐŚĂƐƐŝƐ ĐŽŶƚƌŽů
ĐƚƵĂů ƐƉĞĞĚ ĂĐĐƵƌĂĐLJ ŽĨ Ϭ
ĐƚƵĂů ƐƉĞĞĚ ĂĐĐƵƌĂĐLJ ŽĨ Ϭ
ĞƐĐƌŝƉƚŝŽŶ
ĂƚĂ ƚ
ĂƚĂ ůĞ ϬîϬϴ WŽƐŝƚŝŽ &ƵŶĐƚŝŽ ďLJƚĞϬ DŽǀŝƐŶƉŐĞ Ğ
ŚŝŐŚĞƌ ďLJƚĞϭ DŽǀŝŶŐ
ůŽǁĞƌ ϴ ďLJƚĞϮ /ŶƚĞƌŶĂ
ĂŶŐůĞ Ś
ďŝƚƐ ďLJƚĞϯ /ŶƚĞ ƐƌƚŶĞĂĞ
ĂŶŐůĞ ů ďŝƚƐ
ƐŝŐŶĞĚ
ƐŝŐŶĞĚ
ďLJƚĞϰ | ZĞƐĞƌǀĞ | Ͳ | ϬîϬϬ |
ďLJƚĞϱ | ZĞƐĞƌǀĞ | Ͳ | ϬîϬϬ |
ďLJƚĞϲ | ŽƵŶƚ Ɖ ;ĐŽƵŶƚͿ | ƵŶƐŝŐŶ | ϬͲϮϱϱ ĐŽƵŶƚŝŶŐ ďĞ ĂĚĚĞĚ ŽŶĐĞ ƐĞŶƚ |
ďLJƚĞϳ | WĂƌŝƚLJ ;ĐŚĞĐŬƐ | ƵŶƐŝŐŶ | WĂƌŝƚLJ ďŝƚ |
dŚ ĐĞŽŶƚƌŽů ĨƌĂŵĞ ŝŶĐůƵĚĞƐ ŵŽĚĞ ĐŽŶƚƌŽůůŝŶŐ͕ ŽƉĞŶŶĞƐƐ ŽĨ ůŝŶĞĂƌ ƐƉĞĞĚ͕ ĐŽŶƚƌŽů ŽƉĞŶŶĞƐƐ
&Žƌ ŵŽƌĞ ƉƌŽƚŽĐŽů ĚĞƚĂŝů͕ ƉůĞĂƐĞ ƌĞĨĞƌ ƚŽ
ŽŵŵĂŶĚ EĂŵĞ | ŽŶƚƌŽů ĐŽŵŵĂŶĚ | |||
^ĞŶĚŝŶŐ | ZĞĐĞŝǀŝ | / | LJĐůĞ; | ZĞĐĞͲƚŝŝǀŵĞ ŽƵƚ;ŵƐ |
ĞĐŝͲŵƐĂŝŬŽ ĐŽŶƚƌŽů | ŚĂƐƐŝƐ | ϬîϭϯϬ | ϮϬŵƐ | EŽŶĞ |
ĂƚĂ ůĞ | ϬîϬϴ | |||
WŽƐŝƚŝŽ | &ƵŶĐƚŝŽ | ĂƚĂ ƚ | ĞƐĐƌŝƉƚŝŽŶ | |
ďLJƚĞϬ | ŽŶƚƌŽů | ƵŶƐŝŐŶ | ϬîϬϬ ZĞŵŽƚĞ ĐŽ ϬîϬϭ ŽŵŵĂŶĚ Đ ŵŽĚĞϭ | |
ďLJƚĞϭ | &ĂŝůƵƌĞ ĐŽŵŵĂŶĚ | ƵŶƐŝŐŶ | ^ĞĞ EŽƚĞ Ϯ ĨŽ | |
ďLJƚĞϮ | >ŝŶĞĂƌ ƉĞƌĐĞŶƚ | ƐŝŐŶĞĚ | DĂdžŝŵƵŵ ƐƉĞĞĚ ǀĂůƵĞ Ͳϭƌ͕ĂϭŶϬŐϬĞͿ; | |
ďLJƚĞϯ | /ŶƚĞƌŶĂ ƐƚĞĞƌŝŶ ƉĞƌĐĞŶƚ | ƐŝŐŶĞĚ | DĂdžŝŵƵŵ ŝŶƚĞƌ ĂŶŐ;ͲůϮĞϱ e͕ϮϱeͿ͕ ǀĂů ƌĂŶͲϭŐϬĞϬ;͕ϭϬϬͿ | |
ďLJƚĞϰ | ZĞƐĞƌǀĞ | Ͳ | ϬîϬϬ | |
ďLJƚĞϱ | ZĞƐĞƌǀĞ | Ͳ | ϬîϬϬ | |
ďLJƚĞϲ | ŽƵŶƚ Ɖ ;ĐŽƵŶƚͿ | ƵŶƐŝŐŶ | ϬͲϮϱϱ ĐŽƵŶƚŝŶŐ ǁŝůů ďĞ ĂĚĚĞĚ ĐŽŵŵĂŶĚ ƐĞŶƚ | |
ďLJƚĞϳ | WĂƌŝƚLJ ;ĐŚĞĐŬƐ | ƵŶƐŝŐŶ | WĂƌŝƚLJ ďŝƚ | |
;ĐͿ ĂƐŝĐ ĨƵŶĐƚŝŽŶ ĚĞŵŽŶƐƚƌĂƚŝŽŶ
ϭͿ ĂƌůLJ ƐĞƚƚŝŶŐ
ůŝĚĂƌ ĐŽ͕ ZŶͲ^Ĩ>ŝŝŐ ͲϭƵ ϲƌZ ĂĂƚƐŝ Ž ͘ƐŶ Ă^ŵ ŝƉƵŶůƚĐĞŽĞ ǁ ŝĂƐƌ ĞĂĚĂǀĞƉůƚŽĞĚLJ ŶƚĞŽΖ Ɛ
ůŝ͕Ě Ăŝƌƚ ŝƐ ŶĞĐĞƐƐĂƌZLJ^Ͳ> ŝƚ Žƚ ŽŚZŵ ĂŽǀĚĞŝ ĨĂďLJĚĞ ĂƚŝƚƉƚŽŚƚĞŶĞŽƌ
ƵƚŽ͘ǁĂƌĞ
ĐŽĚĞ ŽĨ
ŽƉ ƚLJŚĞ ƌŽƐͺƌƐůŝĚĂƌ ƉĂĐŬĂŐĞ ƚŽ ƚŚĞ ǁŽƌŬƐƉĂĐĞ
xxxxx://xxx.xxxxxx.xxx/xxxxxxxx/000000/0000.xxxx
dŚĞƌĞ ĂƌĞ ƚǁŽ ŝŵŵƐŽŽ ĚĚŝŝĨŶĨƚŝŐLJŚĐ ĞĂƚ ŝĨŽƌĂŶŵƐĞ͘ůͺ ŝ ŝKĚĂĚŶĂŶ ĞƌĚŽ Ĩ ŝƚŶŚĚĞ ƚŚĞ ϮϳƚŚ ůŝŶĞ ŽĨ ĐŽĚĞ ŝŶ ƌŽƐͺƌƐůŝĚĂƌͬƌƐůŝĚĂƌͺĚ ƉŝƌǀĂƚĞͺŶŚ͘ƉĂƌĂŵ;ΗĨƌĂŵĞͺŝĚΗ͕ ĐŽŶĨŝŐͺ͘ĨƌĂŵĞͺ ĐŚĂŶŐĞ ƚŽ͗
ƉƌŝǀĂƚĞͺŶŚ͘ƉĂƌĂŵ;ΗĨƌĂŵĞͺŝĚΗ͕ ĐŽŶĨŝŐͺ͘ĨƌĂŵĞ
dŚĞ ŽƚŚĞƌ ŝƐ ŵŽĚŝĨLJŝŶŐ ƚŚĞ ŽƵƚƉƵƚ ƚŽƉŝĐ ŽĨ ƌŽƐͺƌƐůŝĚŽĂŝƌŶͬƚƌĐƐůůŽŝƵĚĂͬƌ͗ƐͺƌƉĐͬĐŽŶǀĞƌƚ͘ĐĐ ƉƌŝǀĂƚĞͺŶŚ͘ƉĂƌĂŵ;ΗŽƵƚƉƵƚͺƉŽŝŶƚƐͺƚŽƉŝĐΗ͕ ŽƵ ƐƚĚ͗͗ƐƚƌŝŶŐ;ΗƌƐůŝĚĂƌͺƉŽŝŶƚƐΗͿͿ͖
ĐŚĂŶŐĞ ŝŶƚŽ͗ ƉƌŝǀĂƚĞͺŶŚ͘ƉĂƌĂŵ;ΗŽƵƚƉƵƚͺƉŽŝŶƚƐͺƚŽƉŝĐΗ͕ ŽƵ ƐƚĚ͗͗ƐƚƌŝŶŐ;ΗƉŽŝŶƚƐͺƌĂǁΗͿͿ͖
Ĩer ƚsaving, enter the workspace and compile:
$ cd ~/catkin_ws
$ catkin_make
ŚĂŶŐĞ ůŝĚĂƌ /Wї͕E ĞĐƚůǁїŝŽ ĐƌĂŬŬďї ůKƐĞƉLJƚƐїŝƚ/ŽĞWϰŶŵ Ɛǀ ĞƐƚĞƚŝƚŶŝŐŶŐ
Set the IP to 192.168.1.102, restart computer after the setting is completed.
ϮͿ sĞŚŝĐůĞ ƐƚĂƚƵƐ ĨĞĞĚďƚĂŚĐĞŬ ͕Ŭ ĞĐLJŽďŶŽƚĂƌƌŽĚů
ŽƵƌƐĞ ϭ͗ ^ƚĂƌƚ ƚŚĞ ĐŚĂƐƐŝƐ ĂŶĚ ĐŽŶƚƌŽů
$ roscore
$ rosrun hunter_robot hunter_robot
ŽŶƚƌŽů ďLJ ƚŚĞ ŬĞLJďŽĂƌĚ͗
/ŶƐƚĂůů ƚŚĞ ƉĂĐŬĂŐ ƌĞĞ ĨƚĞĞƌů ĞƚŽŽƉ͗ͺ ƚǁŝƐƚͺŬĞLJďŽĂƌĚ͕ ŚƚƚƉƐ͗ͬͬďůŽŐ͘ĐƐĚŶ͘ŶĞƚͬĂůůŝĂŶƐͬĂƌƚŝĐůĞͬĚĞƚĂ
ŽǁŶůŽĂĚ
$ sudo apt - get install ros - kinetic - teleop - twist - keyboard
^ƚĂƌƚ
$ rosrun teleop_twist_keyboard teleop_twist_keyboard.py
dŚĞ ƐƉĞĞĚ ƐŚŽƵůĚ ŶŽƚ ďĞ ƚŽϬŽ͘ ϮĨŵĂͬƐƐƚ ͕ď LJƌ ĞƉĚƌƵĞĐƐĞƐ ŝƚŶ
ŬĞLJ ŽŶ ƚŚĞ ŬĞLJďŽĂƌĚ͘ ŶĚ ƚŚĞŶ ƚŚĞ ďƵƚƚŽŶ ĐŚĂƐƐŝƐ͘
ŽŶƚƌŽůůŝŶŐ ďLJ ƚŚĞ ŚĂŶĚůĞ͗
/ŶƐƚĂůů ƚŚŶĞŽ ĚƉĞĂ͕Đ ŬƌĂĞŐĨĞĞ ƌũ ŽƚLJŽͺ͗ ŚƚƚƉƐ͗ͬͬďůŽŐ͘ĐƐĚŶ͘ŶĞƚͬŚĂŶͺůͬĂƌƚŝĐůĞͬĚĞƚĂŝů
ŽǁŶůŽĂĚ
$ sudo apt - get install ros - kinetic - joy
^ƚĂƌƚ
$ roslaunch hunter_robot joy.launch
EŽǁ LJŽƵ ĐĂŶ ĐŽŶƚƌŽů ƚŚĞ ĐŚĂƐƐŝƐ ďLJ ůƵĞƚŽŽ
dŚĞ ůƵĞƚŽŽƚŚ ŚĂŶĚůĂĞŽ ŵϮŽϬĚϬƵ ůLJĞƵ ĂŝŶƐͿ ͕ ŝdƚK Wŝ Ɛ; dďĂĞŽƚďƚ ƉŝĐƚƵƌĞ ŽĨ ƚŚĞ ŚĂŶĚůĞ͘
ϮͿ͕ ϯ ůĂƐĞƌ ƉŽŝŶƚ ĐůŽƵĚ ĚĂƚĂ ĂĐƋƵŝƌĞŵĞŶƚ dŚĞƌĞ ĂƌĞ ƚǁŽ ǁ ǁĂŚLJŝƐĐ ŚŽƚ ŶŽĂů ƌŝĐĞŶƌ ĞĞ ĂŵƚĂĞƉ ŵĐĂƌƉĞƐĂƚŝŽŶ ĂŶĚ
ĐƌĞĂtƚĞŝ ŽƵŶĨƐ͘ĨĞ ů ŝŽŶĞ ŵ ŵĂŽƉƐ ͕ƚĐď ůƌĞLJĞĐĂĂƚƵŚŝƐƌĞŽĞ ŶƐ ŽƵĨů ƚŽ ŵŶĂůƉŝ ŶĞ
ĐƌĞĂƚĂŝ ŽďŶŝ ƚŝ ƐĚ ŝ͘ƐĂƉƉŽŝŶƚĞĚ
^ƚĂƌƚ ƵƚŽǁĂƌĞ͗
$ cd Autoware - 1.8.0/ros/
$ ./run
^ƚĂƌƚ ůŝĚĂƌ͗
$ roslaunch rslidar_pointcloud rs_lidar_16.launch
ůŝĐŬ ƚŚĞ ZK^ 'Ě ŽŶ ƵƚŽǁĂƌĞ ŝŶƚĞƌĨĂĐĞ
^ĞůĞĐůƚŝ ĚƚĂŚƌƚĞĂ ͬƉŽǁŝŚŶŝƚĐƐŚͺƚ ƌŽŶĂ ǁĞď ĞĞĚ ƌĐĞůĐŝŽĐƌŬĚ Ğ^Ěƚ͕Ă ƌƚ ƚŽ Ɛƚ ƌĞĐŽƌĚŝŶŐ
ŽŶƚƌŽůůŝŶŐ ƚŚĞ ǀĞŚŝĐůĞ ƚŽ ƌƵŶ Ă ƌŽƵŶĚ ŝŶ ƐůŽǁ ĂƐ ƉŽƐƐŝďůĞ͕ ƌĞŵĞŵďĞƌ ƚŚĞ ůŽĐĂƚŝŽŶ ŽĨ ƚƌĂĐŬŝŶŐ Ă ůĨŽƚŶĞŐƌ ƚƌŚĞĞĐ ŽƚƌĚĂ͕ŝŝ ŝŶůĐŐ͘ůŬ ŝ^Ɛƚ ŽĐƉŽ ŵƚƉŽů ĞƐƚƚĞŽƉ ƌĞĐ ƉƵƚ ƚŚĞ ƌĞĐŽƌĚĞĚ ƉĂĐŬĂŐĞ ŝŶ Ă ĨŽůĚĞƌ͘
ŶƚĞƌ ƚŚĞ ^ŝŵƵůĂƚŝŽŶ ŵŽĚƵůĞ ŽĨ ƵƚŽǁĂƌĞ͕ Ɛ WůĂLJ ĂŶĚ ĐůŝĐŬ WĂƵƐĞ ƚŽ ƉĂƵƐĞ ŝŵŵĞĚŝĂƚĞůLJ͘
ŶƚĞƌ ƚŚĞ ŵĂƉ ŵŽĚƵůĞ Ž ĂĨ ƌ ĞƵĨƚĞŽƌǁĞĂŶƌĐĞĞ͕ ƚƐĨĞ͘ůůĞĂĐƵƚŶ ĐdŚ ƚŽ ĐŽŶŶĞĐƚ ƚŚĞ ǁŽƌůĚ ĐŽŽƌĚŝŶĂƚĞ ƐLJƐƚĞŵ ǁŝƚ ďĂƐĞͺůŝŶŬ ĐŽŽƌĚŝŶĂƚĞ ƐLJƐƚĞŵ ǁŝƚŚ ƚŚĞ ǀĞůŽĚ фͲ͊ͲхͲͲ
фůĂƵŶĐŚх
фŶŽĚĞ ƉƚŬLJŐƉсĞΗсƚΗĨƐΗƚ ĂƚŝĐͺƚƌĂŶƐĨŽƌƚŵŽͺͺƉŵƵĂďƉůΗŝ ƐĂŚƌĞŐƌƐΗс ΗŶϬ Ϭ Ϭ Ϭ Ϭ Ϭ ͬǁŽƌůĚ ͬŵĂƉ ϭϬΗ ͬх
фŶŽĚĞ ƉƚŬLJŐƉсĞΗсƚΗĨƐΗƚ ĂƚŝĐͺƚƌĂŶƐĨŽƌŵͺƉƵďůŝƐŚĞƌΗ Ŷ ĂƌŐƐсΗϬ Ϭ Ϭ Ϭ Ϭ Ϭϭ ϬͬΗď ĂͬƐхĞͺůŝŶŬ ͬǀĞůŽĚLJŶĞ фͬůĂƵŶĐŚх
ŶƚĞƌ ^ĞŶƐŝŶŐ ŵŽĚƵůĞ͕ ĐůŝĐŬ ǀŽƉdžůĞĞůƌͺ͕Ő ƌƚŝŚĚŝͺƐĨ ŝů ĨƵŶĐƚŝŽŶ ŝƐ ƚŚĞ ĨŝůƚƌĂƚŝŽŶ ŽĨ ůŝĚĂƌ ĚĂƚĂ͘
ŶƚĞƌ ŽŵƉƵƚŝŶŐ ŵŽĚƵůĞ
ůŝĐŬ ůͲхŽůĐŝĂĚůŝĂnjƌĂͺͲхƚůŶŝŽĚŽĐƚĂŶͺůŵŝĂnjƉĞƉƌŝŶŐ
zŽƵ ĐĂŶ ƐĞĞ ƚŚĞ ƉƌŽŐƌĞƐƐ ŽĨ ƚŚĞ ŵĂƉ ĐƌĞĂƚŝ
ĂĐŬ ƚŽ ŽŵƉƵƚŝŶŐ ŵŽĚƵůĞ
/Ĩ LJŽƵ ǁĂŶŵƚĂ Ɖƚ͕Ž ŝǀŶŝƐĞƚǁĂ ůƚůŚĞ
$ sudo apt - get install pcl - tools
dŚĞŶ LJŽƵ ĐĂŶ ǀŝĞǁ ƚŚĞ ŵĂƉ͕ WƌĞƐƐ ƚŚĞ ŶƵŵďĞ ƚŚĞ ŵĂƉ ĐŽůŽƌ
$ pcl_viewer Autoware - 200418.pcd
3) ĞŵŽŶƐƚƌĂƚŝŽŶ ŽĨ ŵĂƉƉŝŶŐ͕ ǁĂLJƉŽŝŶƚ ĨƵŶĐƚŝŽŶ
The waypoint recording is also offline, first enter the Simulation module
Enter Map module
Enter Sensing module
Enter Computing module
/Ŷ ƚŚĞ ƚŚŶ ŝƐƌĞĚů ĞƐĐƚĞ ƉĂƐ͕ Ă ĨǀƚŽĞŚů ĞĚƚĞŚ ƌĞƉ ĂƚƉƚŽĂŚ ƚ Śǁ ĂĨLJŝƉ͘ůŽ ĞŝŶƚͺƐĂǀĞƌ
ĂĐŬ ƚŽ ƚŚĞ ^͕ŝ ŵĐƵůůŝĂĐƚŬŝ ŽWŶĂ ƵŵƐŽĞĚ ƵƚůŽĞ ƐƚĂƌƚ ƌĞĐŽƌĚŝ
ĨŽƌ ƚŚĞ ďĂŐ ĨŝŶŝƐŚĞĚ͘ dŚĞŶ LJŽƵ ĐĂŶ ŐĞŶĞƌĂƚ
4) ŽŶƐŝƚŶƌ ϯŐƵ Đ ƚƉŽŝŶƚ ǁĐŝůƚŽŚƵ Ě Ƶ ĂŵƚŶĂŽĚƉǁ ĂǀƌŝĞĞǁ ϯ ƉŽŝ ĐůŽƵĚ ĚĂƚĂ
&ŝůƌ͕LJŝƐ ƚŶ ĞŝĞƐĚ ƚ Ž ƌĞĐŽƌĚ ƚĚŚĂĞƚ ĂƉ ŽƉŝĂŶĐƚŬ ĂĐŐůĞŽ͗ƵĚ
^ƚĂƌƚ ůŝĚĂƌ͗
$ roslaunch rslidar_pointcloud rs_lidar_16.launch Start Autoware :
$ cd Autoware - 1.8.0/ros/
$ ./run
ŶƚĞƌ DĂƉ ŵŽĚƵůĞ͕ ůŽĂĚŝŶŐ ŵĂƉ ĂŶĚ d&
Enter Sensing module, loading point cloud filtering