Image-guided Landmark-based Localization and Mapping with LiDAR
Hernandez Ledesma, Alberto Carlos (2023-03-14)
Image-guided Landmark-based Localization and Mapping with LiDAR
(14.03.2023)
Julkaisu on tekijänoikeussäännösten alainen. Teosta voi lukea ja tulostaa henkilökohtaista käyttöä varten. Käyttö kaupallisiin tarkoituksiin on kielletty.
avoin
Julkaisun pysyvä osoite on:
https://urn.fi/URN:NBN:fi-fe2023031732206
https://urn.fi/URN:NBN:fi-fe2023031732206
Tiivistelmä
Mobile robots must be able to determine their position to operate effectively in diverse
environments. The presented work proposes a system that integrates LiDAR and camera sensors
and utilizes the YOLO object detection model to identify objects in the robot's surroundings. The
system, developed in ROS, groups detected objects into triangles, utilizing them as landmarks to
determine the robot's position. A triangulation algorithm is employed to obtain the robot's position,
which generates a set of nonlinear equations that are solved using the Levenberg-Marquardt
algorithm.
The presented work comprehensively discusses the proposed system's study, design, and
implementation. The investigation begins with an overview of current SLAM techniques. Next, the
system design considers the requirements for localization and mapping tasks and an analysis
comparing the proposed approach to the contemporary SLAM methods. Finally, we evaluate the
system's effectiveness and accuracy through experimentation in the Gazebo simulation
environment, which allows for controlling various disturbances that a real scenario can introduce.
environments. The presented work proposes a system that integrates LiDAR and camera sensors
and utilizes the YOLO object detection model to identify objects in the robot's surroundings. The
system, developed in ROS, groups detected objects into triangles, utilizing them as landmarks to
determine the robot's position. A triangulation algorithm is employed to obtain the robot's position,
which generates a set of nonlinear equations that are solved using the Levenberg-Marquardt
algorithm.
The presented work comprehensively discusses the proposed system's study, design, and
implementation. The investigation begins with an overview of current SLAM techniques. Next, the
system design considers the requirements for localization and mapping tasks and an analysis
comparing the proposed approach to the contemporary SLAM methods. Finally, we evaluate the
system's effectiveness and accuracy through experimentation in the Gazebo simulation
environment, which allows for controlling various disturbances that a real scenario can introduce.