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Semi-supervised generative adversarial network to estimate the depth map of underwater targets in acoustic images for 3D reconstruction

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MEASUREMENT SCIENCE AND TECHNOLOGY 2025年第3期36卷 035403-035403页

作者： Bai, Boming Yan, Weisheng Cui, Rongxin Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Peoples R China

Owing to the unique imaging formulation principle of acoustic cameras, certain dimensions of the target may be lost in acoustic images. Some studies have been conducted to retrieve these missing details from a single pseudo front depth that contains 3D information about the target. Nevertheless, the use of a single pseudo front depth may face ambiguity problems in recovering the 3D information of acoustic targets. Therefore, this paper presents a novel semi-supervised generative adversarial network (SGAN) for estimating the depth of two adjacent acoustic images at know relative sonar poses, which generates high-accuracy reconstruction results, while reducing the dependence on the labeled ground truth of acoustic targets. Specifically, we design a semi-supervised depth estimation framework that utilizes image warping for semi-supervised training, resulting in denser depth maps. Various datasets containing underwater targets and terrain were generated to simulate real underwater scenarios. Experiments with simulator data and real images verify the feasibility of using SGAN for depth estimation in 3D target reconstruction.

关键词： generative adversarial network (GAN) acoustic camera-based 3D reconstruction deep learning for visual perception

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See-Touch-Predict: Active Exploration and Online perception of Terrain Physics With Legged Robots

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第4期10卷 3470-3477页

作者： Lin, Huangxuan Li, Haoyang Gao, Yue Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ MoE Key Lab Artificial Intelligence Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ AI Inst Shanghai 200240 Peoples R China

Assessing physical properties of the environment with vision helps humans to respond appropriately before entering risky areas. However, equipping robots with such perceptual ability is challenging due to the lack of labeled data. To overcome this challenge, we present the Active Exploration and Online perception (AEOP) framework, enabling legged robots to actively estimate and predict physical information of unknown terrains in real-time. The framework contains a learning-based physical sensing policy that controls the robot to actively estimate terrain physics such as friction coefficient and an incremental terrain classifier which performs temporal and spatial consistent terrain segmentation based on color images. A mapping module conjugates physical properties and visual categories of different terrains, building a point cloud map labeled with physical properties at run-time. Extensive real-world experiments were conducted where the proposed framework correctly distinguished a wide range of terrain from slippery (mu=0.17) to rough (mu =1.03) and accurately predicted the friction coefficient of encountered terrains based on vision, providing efficient perception of terrain physics for legged robots.

关键词： Robots Robot sensing systems Legged locomotion Sensors Robot kinematics Foot Training Friction End effectors Physics deep learning for visual perception legged robots

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EdgeFlowNet: 100FPS@1W Dense Optical Flow for Tiny Mobile Robots

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第1期10卷 128-135页

作者： Pinnama Raju, Sai Ramana Kiran Singh, Rishabh Velmurugan, Manoj Jagannatha Sanket, Nitin Worcester Polytech Inst Percept & Autonomous Robot PeAR Grp Robot Engn Worcester MA 01609 USA

Optical flow estimation is a critical task for tiny mobile robotics to enable safe and accurate navigation, obstacle avoidance, and other functionalities. However, optical flow estimation on tiny robots is challenging due to limited onboard sensing and computation capabilities. In this letter, we propose EdgeFlowNet, a high-speed, low-latency dense optical flow approach for tiny autonomous mobile robots by harnessing the power of edge computing. We demonstrate the efficacy of our approach by deploying EdgeFlowNet on a tiny quadrotor to perform static obstacle avoidance, flight through unknown gaps and dynamic obstacle dodging. EdgeFlowNet is about 20x faster than the previous state-of-the-art approaches while improving accuracy by over 20% and using only 1.08 W of power enabling advanced autonomy on palm-sized tiny mobile robots.

关键词： Aerial systems perception and autonomy deep learning for visual perception deep learning for visual perception deep learning deep learning edge computing edge computing optical flow optical flow quadrotors quadrotors vision-based navigation vision-based navigation

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Stereo-Based 3D Human Pose Estimation for Underwater Robots Without 3D Supervision

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第5期10卷 5002-5009页

作者： Wu, Ying-Kun Sattar, Junaed Univ Minnesota Twin Cities Dept Comp Sci & Engn Minneapolis MN 55455 USA Univ Minnesota Twin Cities Minnesota Robot Inst MnRI Minneapolis MN 55455 USA

In this paper, we propose a novel deep learning-based 3D underwater human pose estimator capable of providing metric 3D poses of scuba divers from stereo image pairs. While existing research has made significant advancements in 3D human pose estimation, most methods rely on 3D ground truth for training, which is challenging to acquire in dynamic environments where traditional motion capture systems are impractical to deploy. To overcome this, our approach leverages epipolar geometry to derive 3D information from 2D estimations. Our method estimates semantic keypoints while capturing their corresponding disparity from binocular perspectives, thus avoiding challenges in calibrating for multi-view setups or scale-ambiguity in monocular settings. Additionally, to reduce the sensitivity of our method to 2D annotation accuracy, we propose an auto-refinement pipeline to automatically correct biases introduced by human labeling. Experiments demonstrate that our approach significantly improves performance compared to previous state-of-the-art methods in different environments, including but not limited to underwater scenarios, while being efficient enough to run on limited-capacity edge devices.

关键词： Marine robotics gesture posture and facial expressions deep learning for visual perception deep learning for visual perception

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InstantPose: Zero-Shot Instance-Level 6D Pose Estimation From a Single View

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第6期10卷 6023-6030页

作者： Di Felice, Francesco Remus, Alberto Gasperini, Stefano Busam, Benjamin Ott, Lionel Thalhammer, Stefan Tombari, Federico Avizzano, Carlo Alberto Scuola Super Sant Anna Mech Intelligence Inst Dept Excellence Robot & AI I-56124 Pisa Italy Leonardo SpA Robot Lab Innovat Hub &I ntellectual Property I-16149 Genoa Italy Tech Univ Munich TUM Sch Computat Informat & Technol D-80809 Munich Germany Swiss Fed Inst Technol Dept Mech & Proc Engn Autonomous Syst Lab CH-8092 Zurich Switzerland UAS Tech Vienna Ind Engn Dept A-1040 Vienna Austria Google Zurich CH-8092 Zurich Switzerland

Object pose estimation using visual data is crucial for robotic interaction with the environment. Many existing instance-level methods are restricted by their requirements for 3D CAD models or multiple object views, which limits their flexibility and generalizability. Overcoming this limitation is critical to enhance the adaptability of pose estimation systems. In this work, a novel pipeline that leverages recent advances in reconstruction techniques is presented to address these challenges. To this end, Large Reconstruction Models (LRM) represent an advanced neural architecture capable of generating 3D object models from a limited set of views. Nevertheless, the resulting 3D models often lack relevant geometric and texture details due to insufficient input information. This research presents InstantPose, an innovative zero-shot instance-level pose estimation method that, building upon LRM, can determine the pose of unseen objects using as little as a single unposed RGB reference and RGB-D query images. Extensive experiments demonstrate that InstantPose achieves remarkable performance in object pose estimation on the YCB-V dataset, compared to methods conceived to rely on a geometrically perfect object's model. Furthermore, the 6D pose provided through the presented approach facilitates successful object grasping, highlighting its practical utility in robotic manipulation tasks.

关键词： Pose estimation Three-dimensional displays Solid modeling Accuracy Robots Image reconstruction Feature extraction Semantics Adaptation models Training RGB-D perception deep learning for visual perception deep learning methods

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MonoTher-Depth: Enhancing Thermal Depth Estimation via Confidence-Aware Distillation

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第3期10卷 2830-2837页

作者： Zuo, Xingxing Ranganathan, Nikhil Lee, Connor Gkioxari, Georgia Chung, Soon-Jo CALTECH Pasadena CA 91125 USA

Monocular depth estimation (MDE) from thermal images is a crucial technology for robotic systems operating in challenging conditions such as fog, smoke, and low light. The limited availability of labeled thermal data constrains the generalization capabilities of thermal MDE models compared to foundational RGB MDE models, which benefit from datasets of millions of images across diverse scenarios. To address this challenge, we introduce a novel pipeline that enhances thermal MDE through knowledge distillation from a versatile RGB MDE model. Our approach features a confidence-aware distillation method that utilizes the predicted confidence of the RGB MDE to selectively strengthen the thermal MDE model, capitalizing on the strengths of the RGB model while mitigating its weaknesses. Our method significantly improves the accuracy of the thermal MDE, independent of the availability of labeled depth supervision, and greatly expands its applicability to new scenarios. In our experiments on new scenarios without labeled depth, the proposed confidence-aware distillation method reduces the absolute relative error of thermal MDE by 22.88% compared to the baseline without distillation.

关键词： Predictive models Cameras Training Measurement Depth measurement Adaptation models Metadata Feature extraction Accuracy Uncertainty deep learning for visual perception range sensing thermal camera

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Exploiting Motion Prior for Accurate Pose Estimation of Dashboard Cameras

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第1期10卷 764-771页

作者： Lu, Yipeng Zhao, Yifan Wang, Haiping Ruan, Zhiwei Liu, Yuan Dong, Zhen Yang, Bisheng Wuhan Univ State Key Lab Informat Engn Surveying Mapping & Re Wuhan 430072 Peoples R China China Univ Geosci Wuhan 430079 Peoples R China Didi Chuxing Technol Co Beijing 100193 Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China Nanyang Technol Univ Singapore 639798 Singapore

Dashboard cameras (dashcams) record millions of driving videos daily, offering a valuable potential data source for various applications, including driving map production and updates. A necessary step for utilizing these dashcam data involves the estimation of camera poses. However, the low-quality images captured by dashcams, characterized by motion blurs and dynamic objects, pose challenges for existing image-matching methods in accurately estimating camera poses. In this study, we propose a precise pose estimation method for dashcam images, leveraging the inherent camera motion prior. Typically, image sequences captured by dash cameras exhibit pronounced motion prior, such as forward movement or lateral turns, which serve as essential cues for correspondence estimation. Building upon this observation, we devise a pose regression module aimed at learning camera motion prior, subsequently integrating these prior into both correspondences and pose estimation processes. The experiment shows that, in real dashcams dataset, our method is 22% better than the baseline for pose estimation in AUC5 degrees, and it can estimate poses for 19% more images with less reprojection error in Structure from Motion (SfM).

关键词： Cameras Pose estimation Accuracy Feature extraction Estimation Image coding Videos Neural networks Dynamics Vectors Computer vision for transportation deep learning for visual perception SLAM

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Bayesian NeRF: Quantifying Uncertainty With Volume Density for Neural Implicit Fields

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第3期10卷 2144-2151页

作者： Lee, Sibaek Kang, Kyeongsu Ha, Seongbo Yu, Hyeonwoo Sungkyunkwan Univ Dept Intelligent Robot Suwon 12345 South Korea

We present a Bayesian Neural Radiance Field (NeRF), which explicitly quantifies uncertainty in the volume density by modeling uncertainty in the occupancy, without the need for additional networks, making it particularly suited for challenging observations and uncontrolled image environments. NeRF diverges from traditional geometric methods by providing an enriched scene representation, rendering color and density in 3D space from various viewpoints. However, NeRF encounters limitations in addressing uncertainties solely through geometric structure information, leading to inaccuracies when interpreting scenes with insufficient real-world observations. While previous efforts have relied on auxiliary networks, we propose a series of formulation extensions to NeRF that manage uncertainties in density, both color and density, and occupancy, all without the need for additional networks. In experiments, we show that our method significantly enhances performance on RGB and depth images in the comprehensive dataset. Given that uncertainty modeling aligns well with the inherently uncertain environments of Simultaneous Localization and Mapping (SLAM), we applied our approach to SLAM systems and observed notable improvements in mapping and tracking performance. These results confirm the effectiveness of our Bayesian NeRF approach in quantifying uncertainty based on geometric structure, making it a robust solution for challenging real-world scenarios.

关键词： Uncertainty Neural radiance field Image color analysis Three-dimensional displays Bayes methods Rendering (computer graphics) Estimation Cameras Solid modeling Simultaneous localization and mapping deep learning for visual perception mapping SLAM

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Pair-VPR: Place-Aware Pre-Training and Contrastive Pair Classification for visual Place Recognition With Vision Transformers

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第4期10卷 4013-4020页

作者： Hausler, Stephen Moghadam, Peyman CSIRO CSIRO Robot Data61 Brisbane Qld 4069 Australia Queensland Univ Technol QUT Sch Elect Engn & Robot Brisbane 4069 Australia

In this work we propose a novel joint training method for visual Place Recognition (VPR), which simultaneously learns a global descriptor and a pair classifier for re-ranking. The pair classifier can predict whether a given pair of images are from the same place or not. The network only comprises Vision Transformer components for both the encoder and the pair classifier, and both components are trained using their respective class tokens. In existing VPR methods, typically the network is initialized using pre-trained weights from a generic image dataset such as ImageNet. In this work we propose an alternative pre-training strategy, by using Siamese Masked Image Modeling as a pre-training task. We propose a Place-aware image sampling procedure from a collection of large VPR datasets for pre-training our model, to learn visual features tuned specifically for VPR. By re-using the Mask Image Modeling encoder and decoder weights in the second stage of training, Pair-VPR can achieve state-of-the-art VPR performance across five benchmark datasets with a ViT-B encoder, along with further improvements in localization recall with larger encoders.

关键词： visual place recognition Transformers Decoding Training Image reconstruction Computer vision Benchmark testing Image recognition Data models visualization deep learning for visual perception Recognition Localization

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Taxonomy-Aware Continual Semantic Segmentation in Hyperbolic Spaces for Open-World perception

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第2期10卷 1904-1911页

作者： Hindel, Julia Cattaneo, Daniele Valada, Abhinav Univ Freiburg Dept Comp Sci D-79110 Freiburg Germany

Semantic segmentation models are typically trained on a fixed set of classes, limiting their applicability in open-world scenarios. Class-incremental semantic segmentation aims to update models with emerging new classes while preventing catastrophic forgetting of previously learned ones. However, existing methods impose strict rigidity on old classes, reducing their effectiveness in learning new incremental classes. In this work, we propose Taxonomy-Oriented Poincar & eacute;-regularized Incremental-Class Segmentation (TOPICS) that learns feature embeddings in hyperbolic space following explicit taxonomy-tree structures. This supervision provides plasticity for old classes, updating ancestors based on new classes while integrating new classes at fitting positions. Additionally, we maintain implicit class relational constraints on the geometric basis of the Poincar & eacute;ball. This ensures that the latent space can continuously adapt to new constraints while maintaining a robust structure to combat catastrophic forgetting. We also establish eight realistic incremental learning protocols for autonomous driving scenarios, where novel classes can originate from known classes or the background. Extensive evaluations of TOPICS on the Cityscapes and Mapillary Vistas 2.0 benchmarks demonstrate that it achieves state-of-the-art performance.

关键词： Semantic segmentation Semantics Taxonomy Data models Incremental learning Training Rigidity Adaptation models visualization Predictive models Continual learning incremental learning deep learning for visual perception

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