DESIGN OF A VEHICLE PROTOTYPE FOR A PERFORMANCE TEST OF A LIDAR SIMULTANEOUS LOCALISATION AND MAPPING (SLAM) PROGRAM
Autonomous vehicles are developed to support the development of a sustainable intelligent transport system. The use of autonomous vehicles can reduce operating costs and minimize driving accidents. In industries, autonomous vehicles may increase productivity in the production line. In addition, a...
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/78665 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Autonomous vehicles are developed to support the development of a sustainable
intelligent transport system. The use of autonomous vehicles can reduce operating costs
and minimize driving accidents. In industries, autonomous vehicles may increase
productivity in the production line. In addition, autonomous vehicles can also be assigned
to perform tasks in environments that are dangerous to humans. To keep up with these
technological developments, this undergraduate assignment will examine one of the main
aspects of autonomous vehicles, namely vehicle localization systems, which plays a role in
determining the position of the vehicle.
In this research, the vehicle localization system will be carried out by using several
sensors such as wheel encoders, Inertial Measurement Unit (IMU), and Lidar. The
localization method used is Simultaneous Localisation and Mapping (SLAM) which
combines Lidar scans with odometry references from wheel encoder and IMU sensors. The
SLAM program used is SLAM Karto which is taken from the ROS package for ease of use.
All programs will be tested on a laboratory scale using a simple vehicle prototype.
In this research, a simple vehicle prototype has been successfully designed and built to
test the performance of SLAM Lidar. In this research, SLAM Lidar succeeded in improving
the accuracy of positioning using only the wheel encoder and IMU by 33.3%, with an
average error of 8 cm and a standard deviation of 4 cm. On the other hand, positioning
using the wheel encoder and IMU has an average error of up to 12 cm with a standard
deviation of 9 cm. The SLAM Lidar in this study was also able to form a fairly
representative map with a repeatability rate of up to 80% and a relative error of object shape
of up to 10%.
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