AUV Acceleration Prediction Using DVL and Deep Learning

• URL: http://arxiv.org/abs/2503.16573v1
• Date: Thu, 20 Mar 2025 09:33:47 GMT
• Title: AUV Acceleration Prediction Using DVL and Deep Learning
• Authors: Yair Stolero, Itzik Klein,
• Abstract summary: We present an end-to-end deep learning approach to estimate the AUV acceleration vector based on past DVL velocity measurements.<n>Based on recorded data from sea experiments, we demonstrate that the proposed method improves acceleration vector estimation by more than 65%.
• Score: 2.915868985330569
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Autonomous underwater vehicles (AUVs) are essential for various applications, including oceanographic surveys, underwater mapping, and infrastructure inspections. Accurate and robust navigation are critical to completing these tasks. To this end, a Doppler velocity log (DVL) and inertial sensors are fused together. Recently, a model-based approach demonstrated the ability to extract the vehicle acceleration vector from DVL velocity measurements. Motivated by this advancement, in this paper we present an end-to-end deep learning approach to estimate the AUV acceleration vector based on past DVL velocity measurements. Based on recorded data from sea experiments, we demonstrate that the proposed method improves acceleration vector estimation by more than 65% compared to the model-based approach by using data-driven techniques. As a result of our data-driven approach, we can enhance navigation accuracy and reliability in AUV applications, contributing to more efficient and effective underwater missions through improved accuracy and reliability.

Related papers

• TacoDepth: Towards Efficient Radar-Camera Depth Estimation with One-stage Fusion [54.46664104437454]
  We propose TacoDepth, an efficient and accurate Radar-Camera depth estimation model with one-stage fusion. Specifically, the graph-based Radar structure extractor and the pyramid-based Radar fusion module are designed. Compared with the previous state-of-the-art approach, TacoDepth improves depth accuracy and processing speed by 12.8% and 91.8%.
  arXiv  Detail & Related papers  (2025-04-16T05:25:04Z)
• A Data-Driven Method for INS/DVL Alignment [2.915868985330569]
  Inertial sensors and Doppler velocity logs (DVL) fusion is a promising solution for long-range underwater navigation. We propose an end-to-end deep learning framework for the alignment process.
  arXiv  Detail & Related papers  (2025-03-27T10:38:37Z)
• Gaussian Process Regression for Improved Underwater Navigation [13.221163846643607]
  Doppler velocity logs (DVLs) are typically used to mitigate this drift through velocity measurements.<n>This paper proposes a data-driven alternative based on multi-output Gaussian process regression (MOGPR) to improve DVL velocity estimation.<n>We evaluate our proposed approach using real-world AUV data and compare it against LS and a state-of-the-art deep learning model, BeamsNet.
  arXiv  Detail & Related papers  (2025-02-23T09:13:41Z)
• DCNet: A Data-Driven Framework for DVL Calibration [2.915868985330569]
  We introduce DCNet, a data-driven framework that utilizes a two-dimensional convolution kernel in an innovative way. We demonstrate an average improvement of 70% in accuracy and 80% improvement in calibration time, compared to the baseline approach. Our results also open up new applications for marine robotics utilizing low-cost, high-accurate DVLs.
  arXiv  Detail & Related papers  (2024-10-11T13:47:40Z)
• A Bionic Data-driven Approach for Long-distance Underwater Navigation with Anomaly Resistance [59.21686775951903]
  Various animals exhibit accurate navigation using environment cues. Inspired by animal navigation, this work proposes a bionic and data-driven approach for long-distance underwater navigation. The proposed approach uses measured geomagnetic data for the navigation, and requires no GPS systems or geographical maps.
  arXiv  Detail & Related papers  (2024-02-06T13:20:56Z)
• DVL Calibration using Data-driven Methods [3.4447129363520332]
  We propose an end-to-end deep-learning framework for the calibration procedure. We show that our proposed approach outperforms model-based approaches by 35% in accuracy and 80% in the required calibration time.
  arXiv  Detail & Related papers  (2024-01-23T11:52:25Z)
• Set-Transformer BeamsNet for AUV Velocity Forecasting in Complete DVL Outage Scenarios [10.64241024049424]
  We propose a Set-Transformer-based BeamsNet to regress the current AUV velocity in case of a complete DVL outage. Our approach was evaluated using data from experiments held in the Mediterranean Sea with the Snapir AUV.
  arXiv  Detail & Related papers  (2022-12-22T13:10:44Z)
• Learning-based estimation of in-situ wind speed from underwater acoustics [58.293528982012255]
  We introduce a deep learning approach for the retrieval of wind speed time series from underwater acoustics. Our approach bridges data assimilation and learning-based frameworks to benefit both from prior physical knowledge and computational efficiency.
  arXiv  Detail & Related papers  (2022-08-18T15:27:40Z)
• StreamYOLO: Real-time Object Detection for Streaming Perception [84.2559631820007]
  We endow the models with the capacity of predicting the future, significantly improving the results for streaming perception. We consider multiple velocities driving scene and propose Velocity-awared streaming AP (VsAP) to jointly evaluate the accuracy. Our simple method achieves the state-of-the-art performance on Argoverse-HD dataset and improves the sAP and VsAP by 4.7% and 8.2% respectively.
  arXiv  Detail & Related papers  (2022-07-21T12:03:02Z)
• BeamsNet: A data-driven Approach Enhancing Doppler Velocity Log Measurements for Autonomous Underwater Vehicle Navigation [12.572597882082054]
  This paper proposes BeamsNet, an end-to-end deep learning framework to regress the estimated DVL velocity vector. Our results show that the proposed approach achieved an improvement of more than 60% in estimating the DVL velocity vector.
  arXiv  Detail & Related papers  (2022-06-27T19:38:38Z)
• Real Time Monocular Vehicle Velocity Estimation using Synthetic Data [78.85123603488664]
  We look at the problem of estimating the velocity of road vehicles from a camera mounted on a moving car. We propose a two-step approach where first an off-the-shelf tracker is used to extract vehicle bounding boxes and then a small neural network is used to regress the vehicle velocity.
  arXiv  Detail & Related papers  (2021-09-16T13:10:27Z)
• R4Dyn: Exploring Radar for Self-Supervised Monocular Depth Estimation of Dynamic Scenes [69.6715406227469]
  Self-supervised monocular depth estimation in driving scenarios has achieved comparable performance to supervised approaches. We present R4Dyn, a novel set of techniques to use cost-efficient radar data on top of a self-supervised depth estimation framework.
  arXiv  Detail & Related papers  (2021-08-10T17:57:03Z)
• Deep Learning based Pedestrian Inertial Navigation: Methods, Dataset and On-Device Inference [49.88536971774444]
  Inertial measurements units (IMUs) are small, cheap, energy efficient, and widely employed in smart devices and mobile robots. Exploiting inertial data for accurate and reliable pedestrian navigation supports is a key component for emerging Internet-of-Things applications and services. We present and release the Oxford Inertial Odometry dataset (OxIOD), a first-of-its-kind public dataset for deep learning based inertial navigation research.
  arXiv  Detail & Related papers  (2020-01-13T04:41:54Z)

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.