## 3 Robot Scene Understanding for Navigation Planning
VoxNet [61] is a 3D CNN architecture that was designed to obtain voxel grid representations of the environment using point cloud data. Although this network is capable of detecting objects, object detection alone is not sufficient for scene understanding. Ref. [117] used a LeNet [118] based architecture for semantic segmentation of point clouds. This approach could segment outdoor scenes into seven object categories by voxelisation of input point cloud data. Ref. [119] proposed a method for semantic segmentation of indoor scenes using only depth images. This network jointly predicts the 3D occupancy grid and semantic categories. However, some of the objects were missing during the inference due to a lack of depth details for those objects. In general, point cloud data is irregular; therefore, only representing point cloud data using uniform voxel grids might not accurately interpret real-world information. To address this adverse effect, ref. [120] proposed the OctNet architecture to represent point cloud data in a hierarchical partitioning of space using voxel representations. One of the problems of using voxel grid representations is the requirement of high computational power for varying sparsity of irregular point cloud data. Multi-view representations of 3D point clouds can be employed to reduce the adverse effects of volumetric grid representations. Ref. [121] attempted to generate 2D views of point cloud data, which were then utilised to analyse large urban point cloud data in 3D scenes using deep CNNs. Then, \"tangent convolutions\" were applied to the point cloud data to obtain an image by projecting local geometry to a 2D plane tangent to a point. In general, 2D projective methods are more efficient and scalable than 3D volumetric representations. However, there may be a degree of information loss in the final 3D structure due to inaccuracies in those projections. Instead of converting point cloud data into uniform volumetric representations, processing these point clouds as unstructured data is also a feasible approach [122]. Ref. [59] proposed PointNet, one of the significant point cloud semantic segmentation works. This work shows a degree of permutation invariance to the input point cloud data. It can classify objects by allocating them to categories (e.g., TV, table) and carry out semantic segmentation in scenes. To try to improve on these point-level processing techniques, several works have been carried out that use deep learning methods [123; 124; 125; 126; 127]. Computer memory requirements for 3D voxel representations tend to vary according to the level of voxel resolution. In the presence of sensor noise, the estimation of the occupancy of voxels might be challenging. Instead of using 3D volumetric representations, graph and tree-based representations have been proposed to recover 3D structures from point cloud data. Ref. [128] introduces a 3D graph neural network to semantically segment the environment using 2D RGB image and point cloud data. This method uses both CNNs and graph neural networks to predict semantic classes, with the CNN used to extract features from 2D RGB images. The Superpoints graph method was introduced by [129] to learn scene contextual information and geometry. Superpoints are interest points in a point cloud that are detected and described using a neural network. They are densely sampled and designed to be efficient and robust for use in SLAM applications. First, the input point cloud is separated into geometric elements (named Superpoints), and then these Superpoints are combined considering their mutual features. Finally, graph convolutions are implemented to generate contextual information and semantic labels. This method was shown to be appropriate for the segmentation of large point clouds. Another significant work that processes orderless point cloud data using a permutation invariant deep learning architecture is So-Net [130]. This network extracts features from point clouds hierarchically.