# A Comparison of Segmentation Methods for Semantic OctoMap Generation
## 2 Related Work
### Environment Mapping  
In robotics, environment mapping has evolved from simple 2D occupancy grids to fully 3D representations that fuse geometric and semantic information. OctoMap introduced real-time 3D mapping by organizing space into a probabilistic octree, enabling rapid updates while minimizing the memory consumption [28]. SemanticFusion takes this concept a step further by combining CNN-based pixel labels with dense RGB-D SLAM so that every individual surfel in the 3D map is tagged with a meaningful class name [29].  
The authors of [17] enhanced OctoMap by first employing a finely tuned convolutional neural network to assign a semantic label to every pixel of the RGB image. Through precise sensor calibration, these pixel-wise labels were projected onto the corresponding points of the 16-beam LiDAR scan. Finally, by fusing this semantically annotated point cloud with the robot's odometry within OctoMap, each voxel in the resulting map encoded both an occupancy probability and a semantic class label. In this article, we study this approach thoroughly and check various configurations of such a system using recent advancements in neural-based image segmentation. The method proposed in [30] utilizes OctoMap with semantic information for motion planning. However, semantic information is obtained using the classical SVM-based classification based on color and spatial information. In this context, the method proposed in this article is a natural extension of the method presented in [30].  
In Ref. [31], the authors presented an on-board, under-canopy UAV system that fuses real-time LiDAR-inertial odometry with a RangeNet++ encoder and a lightweight MLP decoder to segment tree trunks, thus enabling the construction of large-scale, fine-grained metric-semantic maps in GPS-denied forest environments.  
Another study [32] showed a TSDF mapping system that fuses LiDAR, camera, and IMU data to generate a dense, semantically labeled 3D mesh in under 10 ms per frame. This map supports real-time localization and terrain driveability analysis, allowing a hybrid A-Star planner to compute smooth, collision-free paths across complex outdoor landscapes.  
Similarly, in [33], the authors deployed DeepLab v3+ with a MobileNet v3 backbone for RGB segmentation, a MobileDet SSD-style detector on both RGB and thermal image streams, and a projection-based SalsaNext network for LiDAR point-cloud segmentation. They fused these outputs into a unified 3D semantic map, and then propagated high-confidence labels from the images onto the LiDAR points, both enriching the map's detail and retraining the LiDAR network for reliable segmentation in new urban and disaster scenarios.  
In recent work [32], a real-time metric-semantic mapping system operating at 30 Hz (less than 7 ms per frame) was presented. It leverages FPGA-synchronized LiDAR, global shutter cameras, and an IMU, fusing LiDAR and visual measurements via an error-state iterated Extended Kalman Filter (EKF) for robust 6-DoF odometry. LiDAR sweeps are undistorted and reprojected into depth/height images. Concurrently, a CNN segmentation head computes per-pixel class probabilities, which are ray-casted and fused into voxel semantic label distributions via recursive Bayesian updates.