A Short Survey on Semantic Occupancy Mapping with 3D-LiDARs in Outdoor Environments

Semantic mapping for mobile robots is a crucial aspect for autonomous navigation and interaction with their environment. Map quality, accuracy of semantic labels and runtime are important dimensions for real-world applications. Voxel models have been widely used to represent occupancy in the map. In recent years, Bayesian Kernel Inference (BKI) has emerged as the main technique on top of traditional occupancy maps to produce smooth maps while maintaining an underlying probabilistic model. Methods based on BKI vary widely in their parameterization and can be tailored to different environments. In this report, we aim to develop the methodology for semantic mapping based on BKI, before evaluating different methods on a range of datasets to assess their parameterization effects for different use cases. We are targeting unstructured outdoor environments where the semantic mapping frameworks need to robustly handle uncertainties in perception.