Feature-PLPD: Feature-Point and Line Points Detection for Real-Time Embedded Visual Odometry-Based Systems

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 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.