Performance evaluation of EKF-SLAM algorithm using an ASC in marine environments

• Main Author: Si, Jian Wen.
• Other Authors: Wang Dan Wei
• Format: Final Year Project
• Language: English
• Published: 2012
• Subjects: DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
• Online Access: http://hdl.handle.net/10356/49472
• Institution: Nanyang Technological University

This report examines how different sensor data captured can be fused to the Simultaneous Localization and Mapping (SLAM) algorithm to improve the navigation of the Autonomous Surface Craft. It will first look into the on-board system and the different sensors used to capture the data. Following which, it will also look into the process and techniques used for data extraction. Feature extraction plays an important part in map building and improving the accuracy of the SLAM algorithm and will be discussed in detail. Concepts and usage of the SLAM algorithm will play a major role in finding ways to fuse the sensor data and improving the algorithm. Simultaneous Localization and Mapping (SLAM) algorithm plays a key role in autonomous vehicles and robotics. Autonomous vehicles make use of this SLAM algorithm to perform autonomous navigation, building an estimated map of the unknown environment and surroundings and also to perform obstacle avoidance while being able to locate its own position estimates. There are different types of SLAM algorithm being used. This report will highlight the experimental results from integrating different sensors to the Autonomous Surface Craft and perform simulation using different SLAM algorithm to improve the performance of the navigation of the Autonomous Surface Craft.