김진환 교수
Jinwhan Kim
KAIST 기계공학과 · 공학
연구실 소개
김진환 교수의 연구실은 해양 및 우주 분야의 자율주행 시스템을 핵심으로 하여, 다중 센서 융합 기반의 목표 탐지·추적 기술과 실시간 상태 추정 기법을 연구하고 있습니다. 특히 무인 항공기 및 무인 해양선박(USV)의 자율 항법, 충돌 회피, 해상 환경 인식 등에 응용 가능한 고정밀 센서 융합 알고리즘과 비선형 추정 기법(예: 페티클 필터, UKF, RBPF)을 개발하고 있습니다. 해양 환경에서의 실시간 비디오 기반 선체 점검 및 다중 모odal 센서 데이터셋 구축을 통해 실제 해양 환경에서의 자율성과 신뢰성을 확보하고자 합니다.
연구 현황
연구 성과 추이
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
주요 논문
15Abstract This study addresses the development of algorithms for multiple target detection and tracking in the framework of sensor fusion and its application to autonomous navigation and collision avoidance systems for the unmanned surface vehicle (USV) Aragon. To provide autonomous navigation capabilities, various perception sensors such as radar, lidar, and cameras have been mounted on the USV platform and automatic ship detection algorithms are applied to the sensor measurements. The relative
During the reentry to the atmosphere, certain ballistic missiles are known to undergo violent spiraling motions induced by aerodynamic resonance between roll and yaw/pitch modes. Successful interception of such spiraling targets is critically dependent on the performance of the target state estimator. Strong nonlinearities involved in the system dynamics and measurement equations together with sensor noise make this a challenging estimation task. The performance of an extended Kalman filter (EKF
Abstract Underwater visual inspection is an important task for checking the structural integrity and biofouling of the ship hull surface to improve the operational safety and efficiency of ships and floating vessels. This paper describes the development of an autonomous in‐water visual inspection system and its application to visual hull inspection of a full‐scale ship. The developed system includes a hardware vehicle platform and software algorithms for autonomous operation of the vehicle. The
This paper presents a multimodal maritime dataset and the data collection procedure used to gather it, which aims to facilitate autonomous navigation in restricted water environments. The dataset comprises measurements obtained using various perception and navigation sensors, including a stereo camera, an infrared camera, an omnidirectional camera, three LiDARs, a marine radar, a global positioning system, and an attitude heading reference system. The data were collected along a 7.5-km-long rout
The performance of ballistic target interception is critically dependent on the performance of the target state estimation. The estimation performance then strongly depends on the accuracy of the measurement model. The Gaussian uncertainty distribution has commonly been used for representing the statistical properties of sensor noise, due to its mathematical simplicity and effectiveness. However, seeker sensor measurements are often corrupted by glint noise which is highly non-Gaussian, and conv
This paper addresses the development of an unmanned surface vehicle (USV) system by Team Angry-Nerds from KAIST for the inaugural Maritime RobotX Challenge competition, which was held on October 20-26, 2014, in Marina Bay, Singapore. The USV hardware was developed on a catamaran platform by integrating various system components, including propulsion, sensors, computer, power, and emergency systems. The competition comprised five mission tasks: 1) navigation and control, 2) underwater search and
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