arXiv subjects

Jaejeong Shin

Publications and source records attributed to Jaejeong Shin.

5 recordsLinked to original sources

arXiv2026

CAVE-NAV: VLM-Based Autonomous 3D Navigation in Underwater Cave Environments

Autonomous navigation in underwater cave environments is essential for search-and-rescue operations, scientific exploration, and emergency egress. Traditional navigation systems commonly depend on dense visual features for localization and mapping. In underwater caves, however, visual degradation can undermine feature-based localization, sonar-based mapping may yield overly conservative obstacle representations, and communication constraints preclude real-time human guidance. To address these limitations, we propose an autonomous underwater cave navigation framework that leverages a vision-language model (VLM) with Chain-of-Thought (CoT) reasoning to infer navigable directions from environmental cues, including light intensity gradients, passage morphology, and geometric complexity, captured through multimodal inputs comprising RGB imagery, depth maps, and sonar-based vertical-clearance measurements, thereby supporting safe 3D navigation through confined cave passages. High-fidelity simulations across multiple cave topologies demonstrate that the proposed framework completes all evaluated end-to-end traversals without collisions while maintaining safe clearance from cave boundaries.

cs.RO↗

arXiv2024

Fast Marching based Rendezvous Path Planning for a Team of Heterogeneous Vehicle

This paper presents a formulation for deterministically calculating optimized paths for a multiagent system consisting of heterogeneous vehicles. The key idea is the calculation of the shortest time for each agent to reach every grid point from its known initial position. Such arrival time map is efficiently computed using the Fast Marching Method (FMM), a computational algorithm originally designed for solving boundary value problems of the Eikonal equation. By leveraging the FMM, we demonstrate that the minimal time rendezvous point and paths for all member vehicles can be uniquely determined with minimal computational overhead. The scalability and adaptability of the present method during online execution are investigated, followed by a comparison with a baseline method that highlights the effectiveness of the proposed approach. Then, the potential of the present method is showcased through a virtual rendezvous scenario involving the coordination of a ship, an underwater vehicle, an aerial vehicle, and a ground vehicle, all converging at the optimal location within the Tampa Bay area in minimal time. The results show that the developed framework can efficiently construct continuous paths of heterogeneous vehicles by accommodating operational constraints via an FMM algorithm

cs.MA↗

arXiv2022

Time and Cost-Efficient Bathymetric Mapping System using Sparse Point Cloud Generation and Automatic Object Detection

Generating 3D point cloud (PC) data from noisy sonar measurements is a problem that has potential applications for bathymetry mapping, artificial object inspection, mapping of aquatic plants and fauna as well as underwater navigation and localization of vehicles such as submarines. Side-scan sonar sensors are available in inexpensive cost ranges, especially in fish-finders, where the transducers are usually mounted to the bottom of a boat and can approach shallower depths than the ones attached to an Uncrewed Underwater Vehicle (UUV) can. However, extracting 3D information from side-scan sonar imagery is a difficult task because of its low signal-to-noise ratio and missing angle and depth information in the imagery. Since most algorithms that generate a 3D point cloud from side-scan sonar imagery use Shape from Shading (SFS) techniques, extracting 3D information is especially difficult when the seafloor is smooth, is slowly changing in depth, or does not have identifiable objects that make acoustic shadows. This paper introduces an efficient algorithm that generates a sparse 3D point cloud from side-scan sonar images. This computation is done in a computationally efficient manner by leveraging the geometry of the first sonar return combined with known positions provided by GPS and down-scan sonar depth measurement at each data point. Additionally, this paper implements another algorithm that uses a Convolutional Neural Network (CNN) using transfer learning to perform object detection on side-scan sonar images collected in real life and generated with a simulation. The algorithm was tested on both real and synthetic images to show reasonably accurate anomaly detection and classification.

cs.CV↗

arXiv2022

Synthetic Sonar Image Simulation with Various Seabed Conditions for Automatic Target Recognition

We propose a novel method to generate underwater object imagery that is acoustically compliant with that generated by side-scan sonar using the Unreal Engine. We describe the process to develop, tune, and generate imagery to provide representative images for use in training automated target recognition (ATR) and machine learning algorithms. The methods provide visual approximations for acoustic effects such as back-scatter noise and acoustic shadow, while allowing fast rendering with C++ actor in UE for maximizing the size of potential ATR training datasets. Additionally, we provide analysis of its utility as a replacement for actual sonar imagery or physics-based sonar data.

cs.CV↗

arXiv2022

Information-Aware Guidance for Magnetic Anomaly based Navigation

In the absence of an absolute positioning system, such as GPS, autonomous vehicles are subject to accumulation of positional error which can interfere with reliable performance. Improved navigational accuracy without GPS enables vehicles to achieve a higher degree of autonomy and reliability, both in terms of decision making and safety. This paper details the use of two navigation systems for autonomous agents using magnetic field anomalies to localize themselves within a map; both techniques use the information content in the environment in distinct ways and are aimed at reducing the localization uncertainty. The first method is based on a nonlinear observability metric of the vehicle model, while the second is an information theory based technique which minimizes the expected entropy of the system. These conditions are used to design guidance laws that minimize the localization uncertainty and are verified both in simulation and hardware experiments are presented for the observability approach.

eess.SY↗