# Semantic Linking Maps for Active Visual Object Search

arXiv: https://arxiv.org/abs/2006.10807 · HTML: https://mlsanity.com/paper/2006.10807/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2020.

## Built on by (most cited first, 18 of 18)

- [ESC: Exploration with Soft Commonsense Constraints for Zero-shot Object Navigation](https://mlsanity.com/paper/2301.13166/) (2023)
- [Semantics for Robotic Mapping, Perception and Interaction: A Survey](https://mlsanity.com/paper/2101.00443/) (2021)
- [Reasoning with Scene Graphs for Robot Planning under Partial Observability](https://mlsanity.com/paper/2202.10432/) (2022)
- [Proactive Robot Assistance via Spatio-Temporal Object Modeling](https://mlsanity.com/paper/2211.15501/) (2022)
- [Advances in Inference and Representation for Simultaneous Localization and Mapping](https://mlsanity.com/paper/2103.05041/) (2021)
- [An Object SLAM Framework for Association, Mapping, and High-Level Tasks](https://mlsanity.com/paper/2305.07299/) (2023)
- [Towards Optimal Correlational Object Search](https://mlsanity.com/paper/2110.09991/) (2021)
- [A System for Generalized 3D Multi-Object Search](https://mlsanity.com/paper/2303.03178/) (2023)
- [Zero-shot Active Visual Search (ZAVIS): Intelligent Object Search for Robotic Assistants](https://mlsanity.com/paper/2209.08803/) (2022)
- [Believing is Seeing: Unobserved Object Detection using Generative Models](https://mlsanity.com/paper/2410.05869/) (2024)
- [Mapping High-level Semantic Regions in Indoor Environments without Object Recognition](https://mlsanity.com/paper/2403.07076/) (2024)
- [SeanNet: Semantic Understanding Network for Localization Under Object Dynamics](https://mlsanity.com/paper/2110.02276/) (2021)
- [MatterDoor: Sampling Zero-shot Spatio-semantic Priors using Generative Models](https://mlsanity.com/paper/2510.11014/) (2025)
- [SEAL: Semantic Frame Execution And Localization for Perceiving Afforded Robot Actions](https://mlsanity.com/paper/2303.14067/) (2023)
- [Counter-Hypothetical Particle Filters for Single Object Pose Tracking](https://mlsanity.com/paper/2305.17828/) (2023)
- [AnyOKP: One-Shot and Instance-Aware Object Keypoint Extraction with Pretrained ViT](https://mlsanity.com/paper/2309.08134/) (2023)
- [The Neural Compass: Probabilistic Relative Feature Fields for Robotic Search](https://mlsanity.com/paper/2603.08544/) (2026)
- [Robots That Take Initiative: A Framework for Building and Evaluating Proactive Robots](https://mlsanity.com/paper/2609.28910/) (2026)

## Abstract (arXiv)

We aim for mobile robots to function in a variety of common human environments. Such robots need to be able to reason about the locations of previously unseen target objects. Landmark objects can help this reasoning by narrowing down the search space significantly. More specifically, we can exploit background knowledge about common spatial relations between landmark and target objects. For example, seeing a table and knowing that cups can often be found on tables aids the discovery of a cup. Such correlations can be expressed as distributions over possible pairing relationships of objects. In this paper, we propose an active visual object search strategy method through our introduction of the Semantic Linking Maps (SLiM) model. SLiM simultaneously maintains the belief over a target object's location as well as landmark objects' locations, while accounting for probabilistic inter-object spatial relations. Based on SLiM, we describe a hybrid search strategy that selects the next best view pose for searching for the target object based on the maintained belief. We demonstrate the efficiency of our SLiM-based search strategy through comparative experiments in simulated environments. We further demonstrate the real-world applicability of SLiM-based search in scenarios with a Fetch mobile manipulation robot.