Vision-in-the-loop Simulation for Deep Monocular Pose Estimation of UAV in Ocean Environment

• URL: http://arxiv.org/abs/2502.05409v1
• Date: Sat, 08 Feb 2025 02:19:42 GMT
• Title: Vision-in-the-loop Simulation for Deep Monocular Pose Estimation of UAV in Ocean Environment
• Authors: Maneesha Wickramasuriya, Beomyeol Yu, Taeyoung Lee, Murray Snyder,
• Abstract summary: This paper proposes a vision-in-the-loop simulation environment for deep monocular pose estimation of a UAV operating in an ocean environment. We present a photo-realistic 3D virtual environment leveraging recent advancements in Gaussian splatting. The resulting simulation enables the indoor testing of flight maneuvers while verifying all aspects of flight software, hardware, and the deep monocular pose estimation scheme.
• Score: 0.21427777919040414
• License:
• Abstract: This paper proposes a vision-in-the-loop simulation environment for deep monocular pose estimation of a UAV operating in an ocean environment. Recently, a deep neural network with a transformer architecture has been successfully trained to estimate the pose of a UAV relative to the flight deck of a research vessel, overcoming several limitations of GPS-based approaches. However, validating the deep pose estimation scheme in an actual ocean environment poses significant challenges due to the limited availability of research vessels and the associated operational costs. To address these issues, we present a photo-realistic 3D virtual environment leveraging recent advancements in Gaussian splatting, a novel technique that represents 3D scenes by modeling image pixels as Gaussian distributions in 3D space, creating a lightweight and high-quality visual model from multiple viewpoints. This approach enables the creation of a virtual environment integrating multiple real-world images collected in situ. The resulting simulation enables the indoor testing of flight maneuvers while verifying all aspects of flight software, hardware, and the deep monocular pose estimation scheme. This approach provides a cost-effective solution for testing and validating the autonomous flight of shipboard UAVs, specifically focusing on vision-based control and estimation algorithms.

Related papers

• FrontierNet: Learning Visual Cues to Explore [54.8265603996238]
  This work aims at leveraging 2D visual cues for efficient autonomous exploration, addressing the limitations of extracting goal poses from a 3D map. We propose a image-only frontier-based exploration system, with FrontierNet as a core component developed in this work. Our approach provides an alternative to existing 3D-dependent exploration systems, achieving a 16% improvement in early-stage exploration efficiency.
  arXiv  Detail & Related papers  (2025-01-08T16:25:32Z)
• OccNeRF: Advancing 3D Occupancy Prediction in LiDAR-Free Environments [77.0399450848749]
  We propose an OccNeRF method for training occupancy networks without 3D supervision. We parameterize the reconstructed occupancy fields and reorganize the sampling strategy to align with the cameras' infinite perceptive range. For semantic occupancy prediction, we design several strategies to polish the prompts and filter the outputs of a pretrained open-vocabulary 2D segmentation model.
  arXiv  Detail & Related papers  (2023-12-14T18:58:52Z)
• Instance-aware Multi-Camera 3D Object Detection with Structural Priors Mining and Self-Boosting Learning [93.71280187657831]
  Camera-based bird-eye-view (BEV) perception paradigm has made significant progress in the autonomous driving field. We propose IA-BEV, which integrates image-plane instance awareness into the depth estimation process within a BEV-based detector.
  arXiv  Detail & Related papers  (2023-12-13T09:24:42Z)
• Calibrating Panoramic Depth Estimation for Practical Localization and Mapping [20.621442016969976]
  The absolute depth values of surrounding environments provide crucial cues for various assistive technologies, such as localization, navigation, and 3D structure estimation. We propose that accurate depth estimated from panoramic images can serve as a powerful and light-weight input for a wide range of downstream tasks requiring 3D information.
  arXiv  Detail & Related papers  (2023-08-27T04:50:05Z)
• Vision-Based Autonomous Navigation for Unmanned Surface Vessel in Extreme Marine Conditions [2.8983738640808645]
  This paper presents an autonomous vision-based navigation framework for tracking target objects in extreme marine conditions. The proposed framework has been thoroughly tested in simulation under extremely reduced visibility due to sandstorms and fog. The results are compared with state-of-the-art de-hazing methods across the benchmarked MBZIRC simulation dataset.
  arXiv  Detail & Related papers  (2023-08-08T14:25:13Z)
• Aerial Monocular 3D Object Detection [67.20369963664314]
  DVDET is proposed to achieve aerial monocular 3D object detection in both the 2D image space and the 3D physical space. To address the severe view deformation issue, we propose a novel trainable geo-deformable transformation module. To encourage more researchers to investigate this area, we will release the dataset and related code.
  arXiv  Detail & Related papers  (2022-08-08T08:32:56Z)
• Semi-Perspective Decoupled Heatmaps for 3D Robot Pose Estimation from Depth Maps [66.24554680709417]
  Knowing the exact 3D location of workers and robots in a collaborative environment enables several real applications. We propose a non-invasive framework based on depth devices and deep neural networks to estimate the 3D pose of robots from an external camera.
  arXiv  Detail & Related papers  (2022-07-06T08:52:12Z)
• Integration of the 3D Environment for UAV Onboard Visual Object Tracking [7.652259812856325]
  Single visual object tracking from an unmanned aerial vehicle poses fundamental challenges. We introduce a pipeline that combines a model-free visual object tracker, a sparse 3D reconstruction, and a state estimator. By representing the position of the target in 3D space rather than in image space, we stabilize the tracking during ego-motion.
  arXiv  Detail & Related papers  (2020-08-06T18:37:29Z)
• Transferable Active Grasping and Real Embodied Dataset [48.887567134129306]
  We show how to search for feasible viewpoints for grasping by the use of hand-mounted RGB-D cameras. A practical 3-stage transferable active grasping pipeline is developed, that is adaptive to unseen clutter scenes. In our pipeline, we propose a novel mask-guided reward to overcome the sparse reward issue in grasping and ensure category-irrelevant behavior.
  arXiv  Detail & Related papers  (2020-04-28T08:15:35Z)

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.