Detection of interest features is a fundamental pre-processing task for visual odometry and visual simultaneous localization and mapping. The combination of line segments and corners is a trend for those applications,...
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Detection of interest features is a fundamental pre-processing task for visual odometry and visual simultaneous localization and mapping. The combination of line segments and corners is a trend for those applications, and it is challenging to meet real-time constraints. To pursue this goal, this letter presents a novel Points and Line Points feature Detection (PLPD) algorithm designed to be highly efficient and accurate for real-time VO-based systems. The proposed algorithm adopts a novel Multi-level Edge Detector (MED) based on gradient intensity and structure tensor that is not confined solely to extracting corners, but rather, it exploits feature-line points properties to extract long-line segments directly without merging collinear fragment segments. The main focus of this work is to fulfill the accuracy, real-time, and embeddability constraints of our proposed method by adopting the gpu-aware software design for a suitable implementation in gpu-based embedded heterogeneous architecture. based on this, we exhibit extensive benchmarking with other state-of-the-art algorithms for feature-line segment extraction and corner detection, showing the efficiency of the proposed algorithm and ensuring a real-time performance up to 33 FPS using our dataset.
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