Position Estimation of Intelligent Artificial Systems Using 3D Point Cloud

Abstract

The three-dimensional reality collected by various sensors such as LiDAR scanners,depth cameras and stereo cameras, is represented by point cloud data. Thecapacity of point clouds to provide rich geometric information about the surroundingsmakes them essential in various applications. Robotics, autonomouscars, augmented reality, virtual reality, and 3D reconstruction all use point clouds.They allow for object detection, localization, mapping, scene comprehension, andlightweight LiDAR SLAM has significant implications for various fields, includingrobotics, autonomous navigation, and augmented reality. Developing compactand efficient LiDAR SLAM systems makes it possible to unlock the potentialof lightweight platforms, enabling their deployment in a wide range of applicationsthat require real-time position mapping, and localization capabilities whileensuring practicality, portability, and cost-effectiveness.immersive visualization. Working with point clouds, on the other hand, presentssubstantial complications. Some primary issues are managing a vast volumeof data, dealing with noise and outliers, dealing with occlusions and missingdata, and conducting efficient processing and analysis. Furthermore, point cloudsfrequently necessitate complicated registration, segmentation, feature extraction,and interpretation methods, necessitating computationally costly processing. Addressingthese issues is critical for realizing the full potential of point cloud datain a variety of real-world applications.SLAM is a key technique in robotics and computer vision that addresses the challengeof estimating a robot�s pose and constructing a map of its environment. Itfinds applications in driverless cars, drones, and augmented reality, enabling autonomousnavigation without external infrastructure or GPS. Challenges includesensor noise, drift, and uncertainty, requiring robust sensor calibration, motionmodeling, and data association. Real-time speed, computing constraints, andmemory limitations are important considerations. Advanced techniques such asfeature extraction, point cloud registration, loop closure detection, and Graph-SLAM optimization algorithms are used. Sensor fusion, map representation, anddata association techniques are vital for reliable SLAM performance.The aim is to create a compact and lightweight LiDAR based SLAM that can beeasily integrated into various platforms without compromising on the accuracyand reliability of SLAM algorithms. Hence, we implemented a lightweight SLAMalgorithm on our dataset with various background situations and a few modificationsto the existing SLAM algorithm to improve the results. We have performedSLAM by using LiDAR sensor without the use of IMU or GPS sensor. The