Browsing by Author "Toy, I."

Now showing 1 - 4 of 4

Citation - WoS: 3
Citation - Scopus: 12
Camera/Lidar Sensor Fusion-Based Autonomous Navigation
(Institute of Electrical and Electronics Engineers Inc., 2024) Yusefi, A.; Durdu, A.; Toy, I.
This research presents a novel approach for autonomous navigation of Unmanned Ground Vehicles (UGV) using a camera and LiDAR sensor fusion system. The proposed method is designed to achieve a high rate of obstacle detection, distance estimation, and obstacle avoidance. In order to thoroughly study the form of things and decrease the problem of object occlusion, which frequently happens in camera-based object recognition, the 3D point cloud received from the LiDAR depth sensors is used. The proposed camera and LiDAR sensor fusion design balance the benefits and drawbacks of the two sensors to produce a detection system that is more reliable than others. The UGV's autonomous navigation system is then provided with the region proposal to re-plan its route and navigate appropriately. The experiments were conducted on a UGV system with high obstacle avoidance and fully autonomous navigation capabilities. The outcomes demonstrate that the suggested technique can successfully maneuver the UGV and detect impediments in actual situations. © 2024 IEEE.
Citation - WoS: 1
Citation - Scopus: 2
Enhanced Obstacle Detection in Autonomous Vehicles Using 3d Lidar Mapping Techniques
(Institute of Electrical and Electronics Engineers Inc., 2024) Tokgoz, M.E.; Yusefi, A.; Toy, I.; Durdu, A.
In this study, a method utilizing a 3D LiDAR(Light Detection and Ranging) sensor for mapping and obstacle detection in autonomous vehicles has been developed. The LiDAR sensor employs laser beams to detect the positions and distances of surrounding objects. Data from the LiDAR were processed to generate 2D maps from the 3D point cloud. During this process, obstacles within the vehicle's navigable height range, as well as those that wouldn't impede its movement were identified. Using a filtering method, points outside of these obstacles were removed to create a map. In experimental studies, it was observed that the developed method can accurately detect challenging obstacles such as fences made of thin wires. Consequently, it is evident that this method holds the potential to offer more reliable and safe obstacle detection for autonomous vehicles. © 2024 IEEE.
Citation - Scopus: 5
Improved Dead Reckoning Localization Using Imu Sensor
(Institute of Electrical and Electronics Engineers Inc., 2022) Toy, I.; Durdu, A.; Yusefi, A.
In the upcoming years, autonomous vehicle technology will advance quickly and spread widely. The most crucial component of such systems is vehicle localization or position estimation. The global navigation satellite system (GNSS) is the one of the most advanced positioning system currently in use. But the GNSS signals could be interrupted or degraded due to interference. Additionally, while GNSS delivers precise information for outdoor systems but is unable to provide a solution for indoor. An indoors-and-outdoors-usable device called an inertial measurement unit (IMU) sensor is employed in this paper to suggest a location estimate technique. The study's proposed IMU dead reckoning method makes use of novel techniques to filter noise data and accurately determine the position of the vehicle. The GNSS-based vehicle localization system can be improved by using this technique in GNSS-denied scenarios. In this paper, the IMU sensor's accelerometer, magnetometer, and gyroscope data are utilized to calculate the vehicle's velocity, orientation, and position. The experimental results on a real autonomous vehicle demonstrate that the system is effective, with average errors in rotation and translation of 1.03 degrees and 1.04 meters, respectively. © 2022 IEEE.
Citation - Scopus: 3
Localization Using Two Different Imu Sensor-Based Dead Reckoning System
(Institute of Electrical and Electronics Engineers Inc., 2024) Toy, I.; Durdu, A.; Yusefi, A.
Dead reckoning estimates the current position, speed, and direction of moving objects using known position information. Localization determines an object's location on the map, categorized into human and vehicle localization. Autonomous vehicles rely on accurate vehicle localization for effective task execution. While Global Navigation Satellite System (GNSS) is a popular method, weak or absent signals can pose challenges. This study utilizes Inertial Measurement Unit (IMU) sensors for localization, integrating a second IMU to enhance accuracy. Fusing data from two IMU sensors, a dead reckoning system achieves 1.02 degrees and 1.41 meters errors in rotation and translation with a single IMU, and 1.01 degrees and 1.04 meters with two IMUs, respectively. © 2024 IEEE.