LiDAR Loop Closure Detection using Semantic Graphs with Graph Attention Networks

• URL: http://arxiv.org/abs/2501.19382v1
• Date: Fri, 31 Jan 2025 18:36:04 GMT
• Title: LiDAR Loop Closure Detection using Semantic Graphs with Graph Attention Networks
• Authors: Liudi Yang, Ruben Mascaro, Ignacio Alzugaray, Sai Manoj Prakhya, Marco Karrer, Ziyuan Liu, Margarita Chli,
• Abstract summary: We propose a novel loop closure detection algorithm that uses graph attention neural networks to encode semantic graphs. We then use semantic registration to estimate the 6 DoF relative pose constraint. For the benefit of the community, we open-source the complete implementation of our proposed algorithm.
• Score: 12.33137981696787
• License:
• Abstract: In this paper, we propose a novel loop closure detection algorithm that uses graph attention neural networks to encode semantic graphs to perform place recognition and then use semantic registration to estimate the 6 DoF relative pose constraint. Our place recognition algorithm has two key modules, namely, a semantic graph encoder module and a graph comparison module. The semantic graph encoder employs graph attention networks to efficiently encode spatial, semantic and geometric information from the semantic graph of the input point cloud. We then use self-attention mechanism in both node-embedding and graph-embedding steps to create distinctive graph vectors. The graph vectors of the current scan and a keyframe scan are then compared in the graph comparison module to identify a possible loop closure. Specifically, employing the difference of the two graph vectors showed a significant improvement in performance, as shown in ablation studies. Lastly, we implemented a semantic registration algorithm that takes in loop closure candidate scans and estimates the relative 6 DoF pose constraint for the LiDAR SLAM system. Extensive evaluation on public datasets shows that our model is more accurate and robust, achieving 13% improvement in maximum F1 score on the SemanticKITTI dataset, when compared to the baseline semantic graph algorithm. For the benefit of the community, we open-source the complete implementation of our proposed algorithm and custom implementation of semantic registration at https://github.com/crepuscularlight/SemanticLoopClosure

Related papers

• Deep Manifold Graph Auto-Encoder for Attributed Graph Embedding [51.75091298017941]
  This paper proposes a novel Deep Manifold (Variational) Graph Auto-Encoder (DMVGAE/DMGAE) for attributed graph data. The proposed method surpasses state-of-the-art baseline algorithms by a significant margin on different downstream tasks across popular datasets.
  arXiv  Detail & Related papers  (2024-01-12T17:57:07Z)
• Embedding Graphs on Grassmann Manifold [31.42901131602713]
  This paper develops a new graph representation learning scheme, namely EGG, which embeds approximated second-order graph characteristics into a Grassmann manifold. The effectiveness of EGG is demonstrated using both clustering and classification tasks at the node level and graph level.
  arXiv  Detail & Related papers  (2022-05-30T12:56:24Z)
• Neighborhood Random Walk Graph Sampling for Regularized Bayesian Graph Convolutional Neural Networks [0.6236890292833384]
  In this paper, we propose a novel algorithm called Bayesian Graph Convolutional Network using Neighborhood Random Walk Sampling (BGCN-NRWS) BGCN-NRWS uses a Markov Chain Monte Carlo (MCMC) based graph sampling algorithm utilizing graph structure, reduces overfitting by using a variational inference layer, and yields consistently competitive classification results compared to the state-of-the-art in semi-supervised node classification.
  arXiv  Detail & Related papers  (2021-12-14T20:58:27Z)
• Joint Graph Learning and Matching for Semantic Feature Correspondence [69.71998282148762]
  We propose a joint emphgraph learning and matching network, named GLAM, to explore reliable graph structures for boosting graph matching. The proposed method is evaluated on three popular visual matching benchmarks (Pascal VOC, Willow Object and SPair-71k) It outperforms previous state-of-the-art graph matching methods by significant margins on all benchmarks.
  arXiv  Detail & Related papers  (2021-09-01T08:24:02Z)
• Sketch-Based Anomaly Detection in Streaming Graphs [89.52200264469364]
  Given a stream of graph edges from a dynamic graph, how can we assign anomaly scores to edges and subgraphs in an online manner? Our method is the first streaming approach that incorporates dense subgraph search to detect graph anomalies in constant memory and time.
  arXiv  Detail & Related papers  (2021-06-08T16:10:36Z)
• Line Graph Neural Networks for Link Prediction [71.00689542259052]
  We consider the graph link prediction task, which is a classic graph analytical problem with many real-world applications. In this formalism, a link prediction problem is converted to a graph classification task. We propose to seek a radically different and novel path by making use of the line graphs in graph theory. In particular, each node in a line graph corresponds to a unique edge in the original graph. Therefore, link prediction problems in the original graph can be equivalently solved as a node classification problem in its corresponding line graph, instead of a graph classification task.
  arXiv  Detail & Related papers  (2020-10-20T05:54:31Z)
• Inverse Graph Identification: Can We Identify Node Labels Given Graph Labels? [89.13567439679709]
  Graph Identification (GI) has long been researched in graph learning and is essential in certain applications. This paper defines a novel problem dubbed Inverse Graph Identification (IGI) We propose a simple yet effective method that makes the node-level message passing process using Graph Attention Network (GAT) under the protocol of GI.
  arXiv  Detail & Related papers  (2020-07-12T12:06:17Z)
• Graph Pooling with Node Proximity for Hierarchical Representation Learning [80.62181998314547]
  We propose a novel graph pooling strategy that leverages node proximity to improve the hierarchical representation learning of graph data with their multi-hop topology. Results show that the proposed graph pooling strategy is able to achieve state-of-the-art performance on a collection of public graph classification benchmark datasets.
  arXiv  Detail & Related papers  (2020-06-19T13:09:44Z)
• Graph Partitioning and Graph Neural Network based Hierarchical Graph Matching for Graph Similarity Computation [5.710312846460821]
  Graph similarity aims to predict a similarity score between one pair of graphs to facilitate downstream applications. We propose a graph partitioning and graph neural network-based model, called PSimGNN, to effectively resolve this issue. PSimGNN outperforms state-of-the-art methods in graph similarity computation tasks using approximate Graph Edit Distance (GED) as the graph similarity metric.
  arXiv  Detail & Related papers  (2020-05-16T15:01:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.