The field of robotics is witnessing significant advancements in 3D mapping and object recognition, driven by innovative approaches to scene understanding, semantic segmentation, and geometric reasoning. Recent developments focus on addressing long-standing challenges such as incomplete scans, occlusion, and inconsistent point cloud density. Researchers are exploring novel frameworks that integrate traversability-aware scene graphs, implicit 3D representations, and multi-task contextual learning to enhance mapping accuracy and robustness. Noteworthy papers in this area include: OV-MAP, which introduces a class-agnostic segmentation model to project 2D masks into 3D space for accurate zero-shot 3D instance segmentation. TACS-Graphs, which proposes a novel framework for traversability-aware consistent scene graphs to achieve more semantically meaningful and topologically coherent segmentation. MCOO-SLAM, which presents a multi-camera omnidirectional object SLAM system that leverages surround-view camera configurations for robust and semantically enriched mapping in complex outdoor scenarios.