# Meaningful Maps With Object-Oriented Semantic Mapping

arXiv: https://arxiv.org/abs/1609.07849 · HTML: https://mlsanity.com/paper/1609.07849/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2016.

## Built on by (most cited first, 30 of 31)

- [In-Place Scene Labelling and Understanding with Implicit Scene Representation](https://mlsanity.com/paper/2103.15875/) (2021)
- [ESLAM: Efficient Dense SLAM System Based on Hybrid Representation of Signed Distance Fields](https://mlsanity.com/paper/2211.11704/) (2022)
- [Contrastive Lift: 3D Object Instance Segmentation by Slow-Fast Contrastive Fusion](https://mlsanity.com/paper/2306.04633/) (2023)
- [Semantics for Robotic Mapping, Perception and Interaction: A Survey](https://mlsanity.com/paper/2101.00443/) (2021)
- [N2F2: Hierarchical Scene Understanding with Nested Neural Feature Fields](https://mlsanity.com/paper/2403.10997/) (2024)
- [ILabel: Interactive Neural Scene Labelling](https://mlsanity.com/paper/2111.14637/) (2021)
- [iDF-SLAM: End-to-End RGB-D SLAM with Neural Implicit Mapping and Deep Feature Tracking](https://mlsanity.com/paper/2209.07919/) (2022)
- [OA-SLAM: Leveraging Objects for Camera Relocalization in Visual SLAM](https://mlsanity.com/paper/2209.08338/) (2022)
- [An Object SLAM Framework for Association, Mapping, and High-Level Tasks](https://mlsanity.com/paper/2305.07299/) (2023)
- [Deep Learning for Visual Localization and Mapping: A Survey](https://mlsanity.com/paper/2308.14039/) (2023)
- [ELLIPSDF: Joint Object Pose and Shape Optimization with a Bi-level Ellipsoid and Signed Distance Function Description](https://mlsanity.com/paper/2108.00355/) (2021)
- [Object-Augmented RGB-D SLAM for Wide-Disparity Relocalisation](https://mlsanity.com/paper/2108.02522/) (2021)
- [Semantic Visual Simultaneous Localization and Mapping: A Survey](https://mlsanity.com/paper/2209.06428/) (2022)
- [On the Overconfidence Problem in Semantic 3D Mapping](https://mlsanity.com/paper/2311.10018/) (2023)
- [VOOM: Robust Visual Object Odometry and Mapping using Hierarchical Landmarks](https://mlsanity.com/paper/2402.13609/) (2024)
- [OpenObj: Open-Vocabulary Object-Level Neural Radiance Fields with Fine-Grained Understanding](https://mlsanity.com/paper/2406.08009/) (2024)
- [S3LAM: Structured Scene SLAM](https://mlsanity.com/paper/2109.07339/) (2021)
- [Semantic Is Enough: Only Semantic Information For NeRF Reconstruction](https://mlsanity.com/paper/2403.16043/) (2024)
- [TSDF++: A Multi-Object Formulation for Dynamic Object Tracking and Reconstruction](https://mlsanity.com/paper/2105.07468/) (2021)
- [Dynamic Semantic Occupancy Mapping using 3D Scene Flow and Closed-Form Bayesian Inference](https://mlsanity.com/paper/2108.03180/) (2021)
- [3D Scene Graph Prediction on Point Clouds Using Knowledge Graphs](https://mlsanity.com/paper/2308.06719/) (2023)
- [EvidMTL: Evidential Multi-Task Learning for Uncertainty-Aware Semantic Surface Mapping from Monocular RGB Images](https://mlsanity.com/paper/2503.04441/) (2025)
- [LatentBKI: Open-Dictionary Continuous Mapping in Visual-Language Latent Spaces with Quantifiable Uncertainty](https://mlsanity.com/paper/2410.11783/) (2024)
- [Topological Semantic Mapping by Consolidation of Deep Visual Features](https://mlsanity.com/paper/2106.12709/) (2021)
- [MCOO-SLAM: A Multi-Camera Omnidirectional Object SLAM System](https://mlsanity.com/paper/2506.15402/) (2025)
- [Efficient Placard Discovery for Semantic Mapping During Frontier Exploration](https://mlsanity.com/paper/2110.14742/) (2021)
- [Artifacts Mapping: Multi-Modal Semantic Mapping for Object Detection and 3D Localization](https://mlsanity.com/paper/2307.01121/) (2023)
- [Semantic Object-level Modeling for Robust Visual Camera Relocalization](https://mlsanity.com/paper/2402.06951/) (2024)
- [Layered Motion Fusion: Lifting Motion Segmentation to 3D in Egocentric Videos](https://mlsanity.com/paper/2506.05546/) (2025)
- [From Reactive to Map-Based AI: Tuned Local LLMs for Semantic Zone Inference in Object-Goal Navigation](https://mlsanity.com/paper/2603.08086/) (2026)

## Abstract (arXiv)

For intelligent robots to interact in meaningful ways with their environment, they must understand both the geometric and semantic properties of the scene surrounding them. The majority of research to date has addressed these mapping challenges separately, focusing on either geometric or semantic mapping. In this paper we address the problem of building environmental maps that include both semantically meaningful, object-level entities and point- or mesh-based geometrical representations. We simultaneously build geometric point cloud models of previously unseen instances of known object classes and create a map that contains these object models as central entities. Our system leverages sparse, feature-based RGB-D SLAM, image-based deep-learning object detection and 3D unsupervised segmentation.