###### Abstract
A fundamental aim of robotic vision is to interpret the semantic information present in a scene to provide scene understanding. Scene understanding goes beyond object detection and requires analysis and elaboration of the data retrieved by the sensors [15]. This concept is used in many practical applications, such as self-driving vehicles, surveillance, transportation, mobile robot navigation, and activity recognition. Understanding scenes using images or videos is a complex problem, however, and requires more steps than just recording and extracting features [16]. Scene understanding can be aided by taking advantage of multiple sensor modalities, which is usually termed multimodal scene understanding [17]. One of the requirements for autonomous robots operating in unstructured environments is the capacity to understand the surrounding environment. Scene understanding comprises subtasks such as depth estimation, scene categorisation, object detection, object tracking, and event categorisation [18]. These scene-understanding sub-tasks can describe different aspects of a scene acquired by perception sensors. In scene understanding, a representation is given to a scene by carrying out some of the above tasks jointly to obtain a holistic understanding of the retrieved scene. To generate this overall representation, the information observed from the above-mentioned scene understanding subtasks must be combined meaningfully. Some of the early methods used to obtain scene understanding included using a block world assumption [19] or bottom-up, top-down inference [20]. Many of these early works depended on heuristics rather than learning-based methods and, thus, were not suitable for generalisation to unstructured real-world scenarios [21]. The most used recent methods try to acquire information from deep learning-based (i.e., convolutional neural networks (CNNs), graph neural networks, vision transformers) approaches. Scene understanding in unstructured outdoor environments using multimodal scene understanding concepts is a challenging task. Many modern scene understanding methods use feature-based high-level representations of environments. In unstructured environments, however, the detection of useful object features is challenging. Therefore, it is difficult to reliably interpret visual information from unstructured or dynamic environments [22]. However, to attain real-world effectiveness, robots should understand their operating environments up to a level that is accurate enough to execute real-time and goal-oriented decisions. One of the key capabilities required for autonomous robot operation is autonomous path planning for robot navigation. Many ground robot navigation applications in unstructured environments require traversal of uneven terrains. Some challenges in robot unstructured environment navigation, including traversal of previously unseen unstructured grounds, can be addressed by adequately designing robot structure, path planningand control [23; 24]. Path planning is generally separated into global and local planning. For global planning, previous knowledge of the operating environment is necessary, and this planning method is also identified as an off-line mode for robot path planning. Robot local path planning, also known as online robot path planning, allows for real-time decisions to be made by the robot in response to the perception of the local environment. Autonomous robot local path planning for optimal terrain traversal in unstructured outdoor environments is an important challenge to solve for robots operating in off-road conditions. This is due to the limitations of standard path planning algorithms, which are incapable of performing the desired tasks in dynamic or unstructured environments where the system lacks prior knowledge and/or already existing maps [25]. Artificial potential fields, simulated annealing, fuzzy logic, artificial neural networks, and dynamic window [26] approaches are some of the algorithms that have been used in robot local path planning [21].