# LSGDDN-LCD: An Appearance-based Loop Closure Detection using Local Superpixel Grid Descriptors and Incremental Dynamic Nodes

arXiv: https://arxiv.org/abs/2304.03872 · HTML: https://mlsanity.com/paper/2304.03872/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 76 references that are in the map.
- First posted on arXiv 8 Apr 2023.

## Builds on (most cited first, 21 of 21)

- [Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances](https://mlsanity.com/paper/1306.0895/) (2013)
- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [Fine-tuning CNN Image Retrieval with No Human Annotation](https://mlsanity.com/paper/1711.02512/) (2017)
- [VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator](https://mlsanity.com/paper/1708.03852/) (2017)
- [DSAC - Differentiable RANSAC for Camera Localization](https://mlsanity.com/paper/1611.05705/) (2016)
- [MAGSAC++, a fast, reliable and accurate robust estimator](https://mlsanity.com/paper/1912.05909/) (2019)
- [DynaSLAM: Tracking, Mapping and Inpainting in Dynamic Scenes](https://mlsanity.com/paper/1806.05620/) (2018)
- [ASLFeat: Learning Local Features of Accurate Shape and Localization](https://mlsanity.com/paper/2003.10071/) (2020)
- [MAGSAC: marginalizing sample consensus](https://mlsanity.com/paper/1803.07469/) (2018)
- [The Revisiting Problem in Simultaneous Localization and Mapping: A Survey on Visual Loop Closure Detection](https://mlsanity.com/paper/2204.12831/) (2022)
- [iBoW-LCD: An Appearance-based Loop Closure Detection Approach using Incremental Bags of Binary Words](https://mlsanity.com/paper/1802.05909/) (2018)
- [AirLoop: Lifelong Loop Closure Detection](https://mlsanity.com/paper/2109.08975/) (2021)
- [Joint detection and matching of feature points in multimodal images](https://mlsanity.com/paper/1810.12941/) (2018)
- [Online Visual Place Recognition via Saliency Re-identification](https://mlsanity.com/paper/2007.14549/) (2020)
- [Fast and Incremental Loop Closure Detection with Deep Features and Proximity Graphs](https://mlsanity.com/paper/2010.11703/) (2020)
- [Fast and Incremental Loop Closure Detection Using Proximity Graphs](https://mlsanity.com/paper/1911.10752/) (2019)
- [HBST: A Hamming Distance embedding Binary Search Tree for Visual Place Recognition](https://mlsanity.com/paper/1802.09261/) (2018)
- [Robust Photogeometric Localization over Time for Map-Centric Loop Closure](https://mlsanity.com/paper/1901.07660/) (2019)
- [DynPL-SVO: A Robust Stereo Visual Odometry for Dynamic Scenes](https://mlsanity.com/paper/2205.08207/) (2022)

## Abstract (arXiv)

Loop Closure Detection (LCD) is an essential component of visual simultaneous localization and mapping (SLAM) systems. It enables the recognition of previously visited scenes to eliminate pose and map estimate drifts arising from long-term exploration. However, current appearance-based LCD methods face significant challenges, including high computational costs, viewpoint variance, and dynamic objects in scenes. This paper introduced an online appearance based LCD using local superpixel grids descriptor and dynamic node, i.e, LSGDDN-LCD, to find similarities between scenes via hand-crafted features extracted from LSGD. Unlike traditional Bag-of-Words (BoW) based LCD, which requires pre-training, we proposed an adaptive mechanism to group similar images called textbftextitdynamic textbftextitnode, which incrementally adjusted the database in an online manner, allowing for efficient and online retrieval of previously viewed images without need of the pre-training. Experimental results confirmed that the LSGDDN-LCD significantly improved LCD precision-recall and efficiency, and outperformed several state-of-the-art (SOTA) approaches on multiple typical datasets, indicating its great potential as a generic LCD framework.