# Particle Filter SLAM for Vehicle Localization

arXiv: https://arxiv.org/abs/2402.07429 · HTML: https://mlsanity.com/paper/2402.07429/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 29 references that are in the map.
- First posted on arXiv 12 Feb 2024.

## Builds on (most cited first, 3 of 3)

- [DeepGI: An Automated Approach for Gastrointestinal Tract Segmentation in MRI Scans](https://mlsanity.com/paper/2401.15354/) (2024)
- [Joint Spatio-Temporal Precoding for Practical Non-Stationary Wireless Channels](https://mlsanity.com/paper/2211.06017/) (2022)
- [Proofs and Supplementary Material: Unified Characterization and Precoding for Non-Stationary Channels](https://mlsanity.com/paper/2202.01827/) (2022)

## Built on by (most cited first, 23 of 23)

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- [BIMCV-R: A Landmark Dataset for 3D CT Text-Image Retrieval](https://mlsanity.com/paper/2403.15992/) (2024)
- [APEER: Automatic Prompt Engineering Enhances Large Language Model Reranking](https://mlsanity.com/paper/2406.14449/) (2024)
- [MemoryMamba: Memory-Augmented State Space Model for Defect Recognition](https://mlsanity.com/paper/2405.03673/) (2024)
- [Adaptive Ensembles of Fine-Tuned Transformers for LLM-Generated Text Detection](https://mlsanity.com/paper/2403.13335/) (2024)
- [TD3 Based Collision Free Motion Planning for Robot Navigation](https://mlsanity.com/paper/2405.15460/) (2024)
- [Cross-Task Multi-Branch Vision Transformer for Facial Expression and Mask Wearing Classification](https://mlsanity.com/paper/2404.14606/) (2024)
- [Incremental Joint Learning of Depth, Pose and Implicit Scene Representation on Monocular Camera in Large-scale Scenes](https://mlsanity.com/paper/2404.06050/) (2024)
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- [Adaptive speed planning for Unmanned Vehicle Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2404.17379/) (2024)
- [The New Agronomists: Language Models are Experts in Crop Management](https://mlsanity.com/paper/2403.19839/) (2024)
- [Image Captioning in news report scenario](https://mlsanity.com/paper/2403.16209/) (2024)
- [InsectMamba: Insect Pest Classification with State Space Model](https://mlsanity.com/paper/2404.03611/) (2024)
- [Multi-Modal UAV Detection, Classification and Tracking Algorithm -- Technical Report for CVPR 2024 UG2 Challenge](https://mlsanity.com/paper/2405.16464/) (2024)
- [The application of Augmented Reality (AR) in Remote Work and Education](https://mlsanity.com/paper/2404.10579/) (2024)
- [Deep Reinforcement Learning for Mobile Robot Path Planning](https://mlsanity.com/paper/2404.06974/) (2024)
- [Machine Learning-Based Vehicle Intention Trajectory Recognition and Prediction for Autonomous Driving](https://mlsanity.com/paper/2402.16036/) (2024)
- [A Survey on Reinforcement Learning Applications in SLAM](https://mlsanity.com/paper/2408.14518/) (2024)
- [End-To-End Training and Testing Gamification Framework to Learn Human Highway Driving](https://mlsanity.com/paper/2404.10849/) (2024)
- [Enhancing Convergence in Federated Learning: A Contribution-Aware Asynchronous Approach](https://mlsanity.com/paper/2402.10991/) (2024)
- [Advanced Feature Manipulation for Enhanced Change Detection Leveraging Natural Language Models](https://mlsanity.com/paper/2403.15943/) (2024)
- [Deep Representation Learning for Multi-functional Degradation Modeling of Community-dwelling Aging Population](https://mlsanity.com/paper/2404.05613/) (2024)
- [Belief-Conditioned One-Step Diffusion: Real-Time Trajectory Planning with Just-Enough Sensing](https://mlsanity.com/paper/2508.12166/) (2025)

## Abstract (arXiv)

Simultaneous Localization and Mapping (SLAM) presents a formidable challenge in robotics, involving the dynamic construction of a map while concurrently determining the precise location of the robotic agent within an unfamiliar environment. This intricate task is further compounded by the inherent "chicken-and-egg" dilemma, where accurate mapping relies on a dependable estimation of the robot's location, and vice versa. Moreover, the computational intensity of SLAM adds an additional layer of complexity, making it a crucial yet demanding topic in the field. In our research, we address the challenges of SLAM by adopting the Particle Filter SLAM method. Our approach leverages encoded data and fiber optic gyro (FOG) information to enable precise estimation of vehicle motion, while lidar technology contributes to environmental perception by providing detailed insights into surrounding obstacles. The integration of these data streams culminates in the establishment of a Particle Filter SLAM framework, representing a key endeavor in this paper to effectively navigate and overcome the complexities associated with simultaneous localization and mapping in robotic systems.