# SeqOT: A Spatial-Temporal Transformer Network for Place Recognition Using Sequential LiDAR Data

arXiv: https://arxiv.org/abs/2209.07951 · HTML: https://mlsanity.com/paper/2209.07951/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 44 references that are in the map.
- First posted on arXiv 16 Sep 2022.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (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)
- [Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition](https://mlsanity.com/paper/2103.01486/) (2021)
- [PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognition](https://mlsanity.com/paper/1804.03492/) (2018)
- [MinkLoc3D: Point Cloud Based Large-Scale Place Recognition](https://mlsanity.com/paper/2011.04530/) (2020)
- [OverlapNet: Loop Closing for LiDAR-based SLAM](https://mlsanity.com/paper/2105.11344/) (2021)
- [OverlapTransformer: An Efficient and Rotation-Invariant Transformer Network for LiDAR-Based Place Recognition](https://mlsanity.com/paper/2203.03397/) (2022)
- [SeqNet: Learning Descriptors for Sequence-based Hierarchical Place Recognition](https://mlsanity.com/paper/2102.11603/) (2021)
- [LoGG3D-Net: Locally Guided Global Descriptor Learning for 3D Place Recognition](https://mlsanity.com/paper/2109.08336/) (2021)
- [Semantic Graph Based Place Recognition for 3D Point Clouds](https://mlsanity.com/paper/2008.11459/) (2020)
- [Self-supervised Point Cloud Prediction Using 3D Spatio-temporal Convolutional Networks](https://mlsanity.com/paper/2110.04076/) (2021)
- [LiDAR Iris for Loop-Closure Detection](https://mlsanity.com/paper/1912.03825/) (2019)
- [NDT-Transformer: Large-Scale 3D Point Cloud Localisation using the Normal Distribution Transform Representation](https://mlsanity.com/paper/2103.12292/) (2021)
- [Intensity Scan Context: Coding Intensity and Geometry Relations for Loop Closure Detection](https://mlsanity.com/paper/2003.05656/) (2020)
- [Range Image-based LiDAR Localization for Autonomous Vehicles](https://mlsanity.com/paper/2105.12121/) (2021)
- [BVMatch: Lidar-based Place Recognition Using Bird's-eye View Images](https://mlsanity.com/paper/2109.00317/) (2021)
- [Condition-Invariant Multi-View Place Recognition](https://mlsanity.com/paper/1902.09516/) (2019)
- [Double-Prong ConvLSTM for Spatiotemporal Occupancy Prediction in Dynamic Environments](https://mlsanity.com/paper/2011.09045/) (2020)
- [A Multi-Domain Feature Learning Method for Visual Place Recognition](https://mlsanity.com/paper/1902.10058/) (2019)
- [Learning an Overlap-based Observation Model for 3D LiDAR Localization](https://mlsanity.com/paper/2105.11717/) (2021)
- [Online Range Image-based Pole Extractor for Long-term LiDAR Localization in Urban Environments](https://mlsanity.com/paper/2108.08621/) (2021)

## Built on by (most cited first, 20 of 20)

- [Self-supervised Learning for Pre-Training 3D Point Clouds: A Survey](https://mlsanity.com/paper/2305.04691/) (2023)
- [CVTNet: A Cross-View Transformer Network for Place Recognition Using LiDAR Data](https://mlsanity.com/paper/2302.01665/) (2023)
- [LCPR: A Multi-Scale Attention-Based LiDAR-Camera Fusion Network for Place Recognition](https://mlsanity.com/paper/2311.03198/) (2023)
- [Fast and Accurate Deep Loop Closing and Relocalization for Reliable LiDAR SLAM](https://mlsanity.com/paper/2309.08086/) (2023)
- [TFNet: Exploiting Temporal Cues for Fast and Accurate LiDAR Semantic Segmentation](https://mlsanity.com/paper/2309.07849/) (2023)
- [PCPNet: An Efficient and Semantic-Enhanced Transformer Network for Point Cloud Prediction](https://mlsanity.com/paper/2304.07773/) (2023)
- [CCL: Continual Contrastive Learning for LiDAR Place Recognition](https://mlsanity.com/paper/2303.13952/) (2023)
- [GSPR: Multimodal Place Recognition Using 3D Gaussian Splatting for Autonomous Driving](https://mlsanity.com/paper/2410.00299/) (2024)
- [Explicit Interaction for Fusion-Based Place Recognition](https://mlsanity.com/paper/2402.17264/) (2024)
- [LRFusionPR: A Polar BEV-Based LiDAR-Radar Fusion Network for Place Recognition](https://mlsanity.com/paper/2504.19186/) (2025)
- [Pose-Graph Attentional Graph Neural Network for Lidar Place Recognition](https://mlsanity.com/paper/2309.00168/) (2023)
- [OverlapMamba: Novel Shift State Space Model for LiDAR-based Place Recognition](https://mlsanity.com/paper/2405.07966/) (2024)
- [A Coarse-to-Fine Place Recognition Approach using Attention-guided Descriptors and Overlap Estimation](https://mlsanity.com/paper/2303.06881/) (2023)
- [Camera-LiDAR Fusion with Latent Contact for Place Recognition in Challenging Cross-Scenes](https://mlsanity.com/paper/2310.10371/) (2023)
- [TSCM: A Teacher-Student Model for Vision Place Recognition Using Cross-Metric Knowledge Distillation](https://mlsanity.com/paper/2404.01587/) (2024)
- [Reliable-loc: Robust sequential LiDAR global localization in large-scale street scenes based on verifiable cues](https://mlsanity.com/paper/2411.07815/) (2024)
- [EDENet: Echo Direction Encoding Network for Place Recognition Based on Ground Penetrating Radar](https://mlsanity.com/paper/2502.20643/) (2025)
- [A Pseudo Global Fusion Paradigm-Based Cross-View Network for LiDAR-Based Place Recognition](https://mlsanity.com/paper/2508.08917/) (2025)
- [Calibrated Keyframe-Segment Selection for Descriptor-Agnostic LiDAR Loop Closure in Repetitive Environments](https://mlsanity.com/paper/2512.09447/) (2025)
- [WHU-PCPR: A cross-platform heterogeneous point cloud dataset for place recognition in complex urban scenes](https://mlsanity.com/paper/2601.06442/) (2026)

## Abstract (arXiv)

Place recognition is an important component for autonomous vehicles to achieve loop closing or global localization. In this paper, we tackle the problem of place recognition based on sequential 3D LiDAR scans obtained by an onboard LiDAR sensor. We propose a transformer-based network named SeqOT to exploit the temporal and spatial information provided by sequential range images generated from the LiDAR data. It uses multi-scale transformers to generate a global descriptor for each sequence of LiDAR range images in an end-to-end fashion. During online operation, our SeqOT finds similar places by matching such descriptors between the current query sequence and those stored in the map. We evaluate our approach on four datasets collected with different types of LiDAR sensors in different environments. The experimental results show that our method outperforms the state-of-the-art LiDAR-based place recognition methods and generalizes well across different environments. Furthermore, our method operates online faster than the frame rate of the sensor. The implementation of our method is released as open source at: https://github.com/BIT-MJY/SeqOT.