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This paper presents an embedded vision system based on reconfigurable hardware (FPGA) to perform stereo image processing and 3D mapping of sparse features for autonomous navigation and obstacle detection in industrial settings. We propose an EKF based visual SLAM to achieve a 6D localization of the vehicle even in non flat scenarios. The system uses vision as the only source of information. As a consequence,...
This paper presents an embedded vision system based on reconfigurable hardware (FPGA) and two CMOS cameras to perform stereo image processing and 3D mapping for autonomous navigation. We propose an EKF based visual SLAM and sparse feature detectors to achieve 6D localization of the vehicle in non flat scenarios. The system can operate regardless of the odometry information from the vehicle since visual...
This paper presents an embedded vision system based on reconfigurable hardware (FPGA) and two CMOS cameras to perform stereo image processing and 3D mapping for autonomous navigation. We propose an EKF based visual SLAM and sparse feature detectors to achieve 6D localization of the vehicle in non flat scenarios. The system can operate regardless of the odometry information from the vehicle since visual...
This paper proposes a stereo vision based localization and mapping strategy for vehicular navigation within industrial environments using natural landmarks. The work proposed is strictly related to factory automation, since focus is on industrial vehicle autonomous navigation for material handling, in order to increase the operating efficiency with reduced risk for accidents. The stereovision system,...
Current approaches to feature detection and matching in images strive to increase the repeatability of the detector and minimize the degree of outliers in the matching. In this paper we present a new approach; we suggest that a lower performance feature detector can produce a result more than adequate for robot navigation irrespectively of the amount of outliers. By using an FPGA together with two...
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