The development of fully autonomous driving vehicles has become a key focus for both industry and academia over the past decade, fostering significant progress in situational awareness abilities and sensor technology. Among various types of sensors, the LiDAR sensor has emerged as a pivotal component in many perception systems due to its long-range detection capabilities, precise 3D range information, and reliable performance in diverse environments. With advancements in LiDAR technology, more reliable and cost-effective sensors have shown great potential for improving situational awareness abilities in widely used consumer products. By leveraging these novel LiDAR sensors, researchers now have a diverse set of powerful tools to effectively tackle the persistent challenges in localization, mapping, and tracking within existing perception systems. This thesis explores LiDAR-based sensor fusion algorithms to address perception challenges in autonomous systems, with a primary focus on dense mapping and global localization using diverse LiDAR sensors. The research involves the integration of novel LiDARs, IMU, and camera sensors to create a comprehensive dataset essential for developing advanced sensor fusion and general-purpose localization and mapping algorithms. Innovative methodologies for global localization across varied environments are introduced. These methodologies include a robust multi-modal LiDAR inertial odometry and a dense mapping framework, which enhance mapping precision and situational awareness. The study also integrates solid-state LiDARs with camera-based deep-learning techniques for object tracking, refining mapping accuracy in dynamic environments. These advancements significantly enhance the reliability and efficiency of autonomous systems in real-world scenarios. The thesis commences with an introduction to innovative sensors and a data collection platform. It proceeds by presenting an open-source dataset designed for the evaluation of advanced SLAM algorithms, utilizing a unique ground-truth generation method. Subsequently, the study tackles two localization challenges in forest and urban environments. Furthermore, it highlights the MM-LOAM dense mapping framework. Additionally, the research explores object-tracking tasks, employing both camera and LiDAR technologies for human and micro UAV tracking.