# DA-RNN: Semantic Mapping with Data Associated Recurrent Neural Networks

arXiv: https://arxiv.org/abs/1703.03098 · HTML: https://mlsanity.com/paper/1703.03098/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2017.

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

- [Semantics for Robotic Mapping, Perception and Interaction: A Survey](https://mlsanity.com/paper/2101.00443/) (2021)
- [MotionSC: Data Set and Network for Real-Time Semantic Mapping in Dynamic Environments](https://mlsanity.com/paper/2203.07060/) (2022)
- [Deep Learning Based 3D Segmentation: A Survey](https://mlsanity.com/paper/2103.05423/) (2021)
- [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)
- [ConvBKI: Real-Time Probabilistic Semantic Mapping Network with Quantifiable Uncertainty](https://mlsanity.com/paper/2310.16020/) (2023)
- [Convolutional Bayesian Kernel Inference for 3D Semantic Mapping](https://mlsanity.com/paper/2209.10663/) (2022)
- [On the Overconfidence Problem in Semantic 3D Mapping](https://mlsanity.com/paper/2311.10018/) (2023)
- [Evidential Semantic Mapping in Off-road Environments with Uncertainty-aware Bayesian Kernel Inference](https://mlsanity.com/paper/2403.14138/) (2024)
- [Dynamic Semantic Occupancy Mapping using 3D Scene Flow and Closed-Form Bayesian Inference](https://mlsanity.com/paper/2108.03180/) (2021)
- [Explicitly incorporating spatial information to recurrent networks for agriculture](https://mlsanity.com/paper/2206.13406/) (2022)
- [Uncertainty-aware Semantic Mapping in Off-road Environments with Dempster-Shafer Theory of Evidence](https://mlsanity.com/paper/2405.06265/) (2024)
- [Topological Semantic Mapping by Consolidation of Deep Visual Features](https://mlsanity.com/paper/2106.12709/) (2021)
- [SeMLaPS: Real-time Semantic Mapping with Latent Prior Networks and Quasi-Planar Segmentation](https://mlsanity.com/paper/2306.16585/) (2023)
- [Atrous Space Bender U-Net (ASBU-Net/LogiNet)](https://mlsanity.com/paper/2212.08613/) (2022)
- [Artifacts Mapping: Multi-Modal Semantic Mapping for Object Detection and 3D Localization](https://mlsanity.com/paper/2307.01121/) (2023)
- [Build Once, Monitor Continuously: Persistent Semantic Mapping via Autonomous Exploration and Open-Vocabulary Object Updates](https://mlsanity.com/paper/2409.15493/) (2024)
- [Robotic Contextual Awareness for Human-Robot Collaboration and Environmental Understanding](https://mlsanity.com/paper/2607.10372/) (2026)

## Abstract (arXiv)

3D scene understanding is important for robots to interact with the 3D world in a meaningful way. Most previous works on 3D scene understanding focus on recognizing geometrical or semantic properties of the scene independently. In this work, we introduce Data Associated Recurrent Neural Networks (DA-RNNs), a novel framework for joint 3D scene mapping and semantic labeling. DA-RNNs use a new recurrent neural network architecture for semantic labeling on RGB-D videos. The output of the network is integrated with mapping techniques such as KinectFusion in order to inject semantic information into the reconstructed 3D scene. Experiments conducted on a real world dataset and a synthetic dataset with RGB-D videos demonstrate the ability of our method in semantic 3D scene mapping.