Simultaneous Localization and Mapping(SLAM)is the process by which a mobile robot can build a map of the surrounding environment and compute its own location.Feature point extraction is one of the key components of a ...Simultaneous Localization and Mapping(SLAM)is the process by which a mobile robot can build a map of the surrounding environment and compute its own location.Feature point extraction is one of the key components of a SLAM system.The extraction accuracy and efficiency of corner detection directly affect the overall accuracy and throughput of the system.However,the complexity of corner detection algorithms makes it challenging to achieve real-time implementation and efficient,low-cost hardware design,especially for mobile robots.Harris corner detection class algorithms including Harris and GFTT(Good Feature to Track)have improved accuracy.However,those algorithms require high resource consumption and latency when implemented on hardware platforms.The GFTT achieves higher accuracy than Harris while requiring higher computational complexity.To address the throughput problem,SFTT(Simple Feature to Track),a new Harris class detection algorithm is proposed,and the corresponding hardware accelerator is designed.The proposed SFTT significantly reduced the computational complexity compared with the Harris algorithm and GFTT.Experiments have shown SFTT also achieved slightly higher accuracy compared with the two algorithms.Furthermore,the GFTT accelerator is designed which reaches up to 325 fps at the frequency of 100 MHz.The proposed design has achieved an improvement in throughput by 1.3×times and power efficiency by 1.7×times as compared to state-of-the-art design.展开更多
基金supported by the National Natural Science Foundation of China under Grant U20A20220.
文摘Simultaneous Localization and Mapping(SLAM)is the process by which a mobile robot can build a map of the surrounding environment and compute its own location.Feature point extraction is one of the key components of a SLAM system.The extraction accuracy and efficiency of corner detection directly affect the overall accuracy and throughput of the system.However,the complexity of corner detection algorithms makes it challenging to achieve real-time implementation and efficient,low-cost hardware design,especially for mobile robots.Harris corner detection class algorithms including Harris and GFTT(Good Feature to Track)have improved accuracy.However,those algorithms require high resource consumption and latency when implemented on hardware platforms.The GFTT achieves higher accuracy than Harris while requiring higher computational complexity.To address the throughput problem,SFTT(Simple Feature to Track),a new Harris class detection algorithm is proposed,and the corresponding hardware accelerator is designed.The proposed SFTT significantly reduced the computational complexity compared with the Harris algorithm and GFTT.Experiments have shown SFTT also achieved slightly higher accuracy compared with the two algorithms.Furthermore,the GFTT accelerator is designed which reaches up to 325 fps at the frequency of 100 MHz.The proposed design has achieved an improvement in throughput by 1.3×times and power efficiency by 1.7×times as compared to state-of-the-art design.
