## 6 Research Challenges and Future Directions in Unstructured Outdoor Environment Navigation
### Future Research Directions  
Multimodal sensor fusion for robot vision provides more robust and reliable environment perception and scene understanding results than mono-sensor robot vision strategiesin unstructured environments. Cameras and LiDARs are usually employed to retrieve RGB and depth data of external environments, respectively. However, high-resolution LiDAR sensors are costly, and their depth data sparsity increases when the observation range is increased. In contrast to mechanical LiDARs, solid-state LiDARs can provide highly accurate and higher-resolution depth data. Their costs are also likely to be significantly lower than mechanical spinning LiDARs, but these sensors are still at the research stage. The development and commercialisation of solid-state LiDARs will likely be pivotal for future advancements in robotic unstructured environment perception.  
In robot navigation, domain change can involve training a model on simulated data and then fine-tuning it on real-world data to improve its performance in the real world. Robot domain adaptability can be enhanced by using techniques such as transfer learning, adversarial training, or domain adversarial neural networks. Researchers have proposed techniques such as data augmentation, feature alignment, or domain adaptation loss functions to address the domain gap. However, domain adaptation remains an active area of research in robotics, and there is still much to be done to improve the performance of machine-learning models in real-world settings. End-to-end learning architectures for robot navigation are appropriate for understanding robot tasks in the local motion planning context. For long-range navigation and in different environment domains, learning-based methods in combination with the classical hierarchical navigation pipeline show promise to produce more robust and safe results.  
Robot environment perception, analysis and scene understanding are critical for robots to generate useful maps and make intelligent decisions. Recent research works such as Kimera [105] and Kimera-multi [106] are able to generate high-level geometric and hierarchical semantic environment understanding for robots. Robot systems that consist of geometric and hierarchical scene representations combined with incremental scene understanding will be advantageous in future robot outdoor unstructured navigation applications. However, the degree of scalability and adaptability of these approaches in unstructured environments with vegetation remains an open research question. In vegetated unstructured environments, obtaining feature-based scene understanding is challenging. Many SLAM methods provide rich geometric data but may incorrectly identify grass-covered terrains as not being traversable and muddy areas as traversable. Therefore, the combination of LiDAR-based geometric/semantic maps with the assistance of reinforcement learning of ego-centric images for robot local path planning could be a promising direction for more robust outdoor unstructured environment navigation. This approach will combine robot SLAM with image-based scene understanding to generate higher levels of spatial understanding of local environments for robots to be able to generate more accurate and safe movements. Overall, our comprehensive review of the literature has indicated that further research into multimodal sensor fusion techniques and deep learning-based scene understanding and task planning methods provides the most promise towards achieving the goal of fully autonomous navigation in outdoor unstructured environments.