## 5 Summary of the Current State-of-the-Art Techniques  
Autonomous navigation can be broadly categorised into the classical modular pipeline methods and end-to-end learning approaches. The modular pipeline approach has limitations due to the requirement for high levels of human intervention in designing the modules, the loss of information through each module, and the overall lack of robustness when conditions vary beyond anticipated limits. The end-to-end learning approach suffers from problems such as over-fitting and poor generalisation. Both modular and end-to-end robot navigation approaches rely on sensors to capture information about the environment or internal robot attributes. Researchers have investigated different sensor modalities to improve robot perception, including raw input types such as sound, pressure, light, and magnetic fields, as well as common robot perception sensor modalities such as cameras, LiDAR, radar, sonar, GNSS, IMU, and odometry sensors. It is crucial to have a reliable real-time understanding of external 3D environments to ensure safe robot navigation. While cameras are commonly used in mobile robotics, relying on a single sensor is not always robust. Different sensor modalities are often combined in robotic applications to improve perception reliability and robustness. Camera and LiDAR fusion has been a popular approach in the robotics community. This fusion method has shown better performance than other vision-based dual sensor fusion approaches, and recent advances in deep learning algorithms have further improved its performance. Monocular and stereo cameras are commonly used with LiDAR sensors to fuse images and point cloud data. However, the technical challenges and cost of sensors and processing power requirements still limit the widespread application of these methods for regular use. Computer vision has experienced rapid growth in the past decade, with deep learning methods accelerating its development. Object detection, depth estimation, semantic segmentation, instance segmentation, scene reconstruction, motion estimation, object tracking, scene understanding, and end-to-end learning are among the subtopics of computer vision. These methods have been widely applied in autonomous navigation, but their accuracy and reliability can be limited, prompting ongoing efforts to improve their quality. Benchmarking datasets, such as KITTI, Waymo, A2D2, nuScenes, Cityscapes, RELLIS-3D, RUGD, Freiburg [29], and WildDash [162], have been used to compare the performance of different vision methods in autonomous urban driving and off-road driving applications.  
Semantic SLAM techniques provide geometric and semantic details of external environments. However, existing semantic SLAM techniques may not be adequate for the safe navigation of robots in outdoor unstructured environments due to challenges in feature detection, uneven terrain conditions (which can lead to robot localisation errors, detection errors, etc.) and effects of wind on vegetation, which can lead to sensor noise and ambiguous results. Therefore, terrain traversability analysis and improved scene understanding using deep learning methods have been developed that are showing promise in assisting robot navigation in outdoor unstructured environments.  
Many classical robot path-planning approaches are used for navigation in local environments. These classical approaches rely on a modular architecture to perceive the environment, plan paths relative to generated maps, and follow trajectories. However, these classical methods are often challenged in dynamic or deformable environments and are not well-suited for off-road navigation conditions where terrains may be unpredictable. Learning-based methods have been developed to improve robot path-planning abilities under different environmental conditions and have shown promise in addressing these challenges. In particular, learning-based navigation methods have been shown to be better suited for off-road navigation conditions, where terrains can be highly variable and unpredictable.