## 6 Research Challenges and Future Directions in Unstructured Outdoor Environment Navigation
### Research Challenges  
SLAM methods have been extensively used for robot navigation applications in indoor environments. Urban autonomous navigation systems also implement SLAM algorithms successfully for sophisticated motion planning. Only a few applications have demonstrated the use of SLAM for dirt roads and off-road uneven terrain navigation. Many visual SLAM techniques are unstable in off-road environments with trees, bushes, uneven terrain and cluttered objects. Application of classical SLAM approaches in off-road environments with vegetation is challenging because of the irregular and dynamically varying structure of the natural environment. LiDAR-based methods may detect traversable regions as geometric obstacles. Therefore, existing SLAM research on robot navigation in off-road unstructured environments is limited. Several feature-based SLAM algorithms fail due to reasons such as the absence of specific features or the loss of detected features caused by the dynamic movement of vegetation due to wind. In general, mobile robots in off-road environments in the presence of vegetation use GNSS with IMUs to estimate the real-time position of robots. In relatively open tree canopies, GNSS sensors can establish a consistent communication link with satellites. The GNSS positioning accuracy in sparse tree canopy environments can be decimeter level [163]. However, under dense tree canopies, GNSS may lose location data due to potential signal interruptions. Visual SLAM (VSLAM) uses features or pixel intensity in images to generate information about environments. Many visual SLAM methods employ feature-based (e.g., points, lines, corners, planes) algorithms for detecting objects or developing a higher-level understanding of environments. In general, monocular, stereo and RGB-D cameras are used in VSLAM. RGB-D camera SLAM is restricted to indoor applications because of its sensitivity to ambient light conditions. SLAM approaches that use event cameras are still not well established. Many point-feature-based SLAM methods are not robust in low-textured scenes such as plane surfaces and are vulnerable to illumination changes. Line features are less sensitive to changes in lighting conditions than point features. Plane features are often found in the artificial environment but are not regularly present in natural environments. Using pixel intensities instead of features in SLAM is more robust to changes in lighting conditions and results in more scene information. Modern semantic VSLAM methods are mostly restricted to specific environments such as indoor or urban driving. Many VSLAM approaches focus on feature-based techniques to generate maps and localise robots. In an off-road environment with vegetation, feature extraction is challenging. The number of distinguishable features in these environments might be low due to the unstructured nature of natural environments. The presence of occlusions, shadows and illumination variation in off-road unstructured environments can obstruct useful feature detection in VSLAM. Therefore, obtaining a higher level of semantic understanding is more complex than understanding urban driving scenarios. In order to improve VSLAM performance in vegetated, outdoor, and unstructured environments, one can train deep learning models to understand features using a large dataset. However, there are difficulties in adapting these networks to unseen environments [164]. LiDAR SLAM is more robust than VSLAM in varying illumination conditions and under shadows. Point cloud registration and feature-based methods are the most common LiDAR SLAM approaches [165]. The LiDAR SLAM point cloud registration approach is more robust in vegetated natural environment mapping than the feature-based method. An adverse effect of LiDAR SLAM is the loss of semantic details in the environment. Researchers have introduced camera and LiDAR sensor fusion-based SLAM concepts to compensate for these issues. There are two separate LiDAR-camera fused SLAM directions, loosely coupled and tightly coupled.