## 6 Research Challenges and Future Directions in Unstructured Outdoor Environment Navigation
### Research Challenges
In loosely coupled SLAM systems, the modalities use two independent approaches for extracting features and high-level information to combine the two information streams. Computations in loosely coupled systems are less complex and generate faster results than in tightly coupled systems. However, these systems may produce low accuracies due to complementary information losses in independent sensor data processing steps. Tightly-coupled models fuse the two forms of sensor data into one framework to generate feature detection or higher-level semantic understanding. Therefore, this approach is more robust in challenging environments. However, these methods are more complex and need high computer memory and GPU requirements. In unstructured outdoor environments, robots require perception, scene understanding and identification of terrain regions suitable for robot navigation. If robot working environments are dynamic, achieving these tasks will be more challenging. Robot scene understanding requires a robot to be able to perceive, analyse, and interpret its working environment. Scene understanding relies on subtopics such as object recognition, semantic segmentation, instance segmentation and scene representations of images or videos. Robot scene understanding is a continuous process, and robots need to be able to learn and adapt to new environments and situations over time. Implementation of machine learning algorithms in scene understanding has increased the possibilities of generating more sophisticated learning-based architectures. However, in general, these methods have high computational requirements. Object recognition in unstructured environments is challenging due to the higher frequency of object occlusions, similar features in different objects, indistinct object boundaries and varying lighting conditions. Semantic segmentation of an unstructured environment is another essential part of understanding the environment, and it is more complex than segmentation in urban environment scenarios due to the variation of different semantic classes. It is challenging to extract fine-grained semantic details from unstructured outdoor environments. These environments have wide semantic diversity, and training robots using large, annotated datasets is challenging due to the limited availability and difficulty of creating such datasets. Researchers use instance segmentation algorithms to acquire a semantically rich understanding of environments, relationships between objects, and contexts. However, limited research has been conducted on robot instance segmentation of urban road environments, and these methods require high computational requirements and are not robust in off-road scenarios. Robots should learn how to robustly interpret environments over time using scene-understanding information to overcome these limitations. Point-based, graph-based, tree-based, multi-view projections, image-based, and volumetric representations have been developed to achieve scene understanding beyond basic perception. These techniques can comprehend environments beyond what can be inferred from a single image. Further investigation of the above-mentioned scene representations is necessary to learn and evolve scene interpretations over time. Novel concepts need to be developed and investigated to extract information from scenes at the object and semantic levels. Robot domain identification and adaptation are valuable for real-world applications to improve performance and achieve more sophisticated navigation capability. In general, robots require the adaptability of learning-based navigation models. Therefore, networks that are trained on one domain (e.g., simulated environment [166], urban city environment) need to identify and adapt to changes in the application domain (e.g., real world, rural off-road environment). These domain gaps can be expressed as one challenge for robot navigation in unstructured environments.