Reliability-Adaptive Multimodal Relative Pose Tracking for Underwater Leader-Follower Robots
Overview
This paper presents a reliability-adaptive multimodal perception framework for six-degree-of-freedom relative-pose tracking between two moving underwater robots. The framework integrates visual, acoustic-ranging, and hydrodynamic measurements while adaptively weighting modality contributions based on time-varying reliability. Extensive experiments using a custom-built AUV platform across 648 trials show an average translational error of 3.8 cm and orientation error of 2.4 degrees. Onboard NVIDIA Jetson AGX Orin, the fusion network achieves 17.62 ms inference latency, offering a 2.69-fold speedup over a Transformer-based baseline. Closed-loop tests confirm stable formation maintenance under computational constraints, and a multimodal dataset from all trials will be released publicly.
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Reported quantity · trials: 648 other · reported trials
“stic-ranging, and hydrodynamic measurements while accounting for their distinct physical characteristics and time-varying reliability. It adaptively weights modality contributions and models temporal dependencies with linear computational complexity, enabling efficient pose estimation on embedded hardware. Extensive experiments were conducted using a custom-built AUV platform, with 648 pool trials spanning two leader”
Exact source · revision 1Source owner not reported
Reported quantity · translational error: 3.8 other · Basis not reported
“vehicle types, three illumination conditions, and nine trajectory patterns. Evaluation yields an average translational error of 3.8 cm and an average orientation error of 2.4{\deg}. On an onboard NVIDIA Jetson AGX Orin, the fusion network achieves an inference latency of 17.62 ms, providing a 2.69-fold speedup over the evaluated Transformer-based fusion baseline. Closed-loop experiments further demonstrate stable fo”
Exact source · revision 1Source owner not reported
Reported quantity · orientation error: 2.4 other · Basis not reported
“vehicle types, three illumination conditions, and nine trajectory patterns. Evaluation yields an average translational error of 3.8 cm and an average orientation error of 2.4{\deg}. On an onboard NVIDIA Jetson AGX Orin, the fusion network achieves an inference latency of 17.62 ms, providing a 2.69-fold speedup over the evaluated Transformer-based fusion baseline. Closed-loop experiments further demonstrate stable fo”
Exact source · revision 1Source owner not reported
Reported quantity · inference latency: 17.62 other · Basis not reported
“vehicle types, three illumination conditions, and nine trajectory patterns. Evaluation yields an average translational error of 3.8 cm and an average orientation error of 2.4{\deg}. On an onboard NVIDIA Jetson AGX Orin, the fusion network achieves an inference latency of 17.62 ms, providing a 2.69-fold speedup over the evaluated Transformer-based fusion baseline. Closed-loop experiments further demonstrate stable fo”
Exact source · revision 1Source owner not reported
Reported quantity · speedup factor: 2.69 other · Basis not reported
“vehicle types, three illumination conditions, and nine trajectory patterns. Evaluation yields an average translational error of 3.8 cm and an average orientation error of 2.4{\deg}. On an onboard NVIDIA Jetson AGX Orin, the fusion network achieves an inference latency of 17.62 ms, providing a 2.69-fold speedup over the evaluated Transformer-based fusion baseline. Closed-loop experiments further demonstrate stable fo”
Exact source · revision 1Source owner not reported
Reported quantity · leader vehicle types: 2 other · Basis not reported
“stic-ranging, and hydrodynamic measurements while accounting for their distinct physical characteristics and time-varying reliability. It adaptively weights modality contributions and models temporal dependencies with linear computational complexity, enabling efficient pose estimation on embedded hardware. Extensive experiments were conducted using a custom-built AUV platform, with 648 pool trials spanning two leader”
Exact source · revision 1Source owner not reported
Artifact availability · dataset: reported pending
“rmation maintenance with a moving leader under onboard computational constraints. A multimodal dataset containing measurements from all 648 trials will be made publicly available.”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalReliability-Adaptive Multimodal Relative Pose Tracking for Underwater Leader-Follower Robots
This paper presents a reliability-adaptive multimodal perception framework for six-degree-of-freedom relative-pose tracking between two moving underwater robots. The framework integrates visual, acoustic-ranging, and hydrodynamic measurements while adaptively weighting modality contributions based on time-varying reliability. Extensive experiments using a custom-built AUV platform across 648 trials show an average translational error of 3.8 cm and orientation error of 2.4 degrees. Onboard NVIDIA Jetson AGX Orin, the fusion network achieves 17.62 ms inference latency, offering a 2.69-fold speedup over a Transformer-based baseline. Closed-loop tests confirm stable formation maintenance under computational constraints, and a multimodal dataset from all trials will be released publicly.
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