arXiv subjects

Sung-Hyun Lee

Publications and source records attributed to Sung-Hyun Lee.

3 recordsLinked to original sources

arXiv2023

Automatic Internal Stray Light Calibration of AMCW Coaxial Scanning LiDAR Using GMM and PSO

In this paper, an automatic calibration algorithm is proposed to reduce the depth error caused by internal stray light in amplitude-modulated continuous wave (AMCW) coaxial scanning light detection and ranging (LiDAR). Assuming that the internal stray light generated in the process of emitting laser is static, the amplitude and phase delay of internal stray light are estimated using the Gaussian mixture model (GMM) and particle swarm optimization (PSO). Specifically, the pixel positions in a raw signal amplitude map of calibration checkboard are segmented by GMM with two clusters considering the dark and bright image pattern. The loss function is then defined as L1-norm of difference between mean depths of two amplitude-segmented clusters. To avoid overfitting at a specific distance in PSO process, the calibration check board is actually measured at multiple distances and the average of corresponding L1 loss functions is chosen as the actual loss. Such loss is minimized by PSO to find the two optimal target parameters: the amplitude and phase delay of internal stray light. According to the validation of the proposed algorithm, the original loss is reduced from tens of centimeters to 3.2 mm when the measured distances of the calibration checkboard are between 1 m and 4 m. This accurate calibration performance is also maintained in geometrically complex measured scene. The proposed internal stray light calibration algorithm in this paper can be used for any type of AMCW coaxial scanning LiDAR regardless of its optical characteristics.

eess.IV↗

arXiv2022

Towards Light-weight and Real-time Line Segment Detection

Previous deep learning-based line segment detection (LSD) suffers from the immense model size and high computational cost for line prediction. This constrains them from real-time inference on computationally restricted environments. In this paper, we propose a real-time and light-weight line segment detector for resource-constrained environments named Mobile LSD (M-LSD). We design an extremely efficient LSD architecture by minimizing the backbone network and removing the typical multi-module process for line prediction found in previous methods. To maintain competitive performance with a light-weight network, we present novel training schemes: Segments of Line segment (SoL) augmentation, matching and geometric loss. SoL augmentation splits a line segment into multiple subparts, which are used to provide auxiliary line data during the training process. Moreover, the matching and geometric loss allow a model to capture additional geometric cues. Compared with TP-LSD-Lite, previously the best real-time LSD method, our model (M-LSD-tiny) achieves competitive performance with 2.5% of model size and an increase of 130.5% in inference speed on GPU. Furthermore, our model runs at 56.8 FPS and 48.6 FPS on the latest Android and iPhone mobile devices, respectively. To the best of our knowledge, this is the first real-time deep LSD available on mobile devices. Our code is available.

cs.CV↗

arXiv2021

Highly precise AMCW time-of-flight scanning sensor based on digital-parallel demodulation

In this paper, a novel amplitude-modulated continuous wave (AMCW) time-of-flight (ToF) scanning sensor based on digital-parallel demodulation is proposed and demonstrated in the aspect of distance measurement precision. Since digital-parallel demodulation utilizes a high-amplitude demodulation signal with zero-offset, the proposed sensor platform can maintain extremely high demodulation contrast. Meanwhile, as all cross correlated samples are calculated in parallel and in extremely short integration time, the proposed sensor platform can utilize a 2D laser scanning structure with a single photo detector, maintaining a moderate frame rate. This optical structure can increase the received optical SNR and remove the crosstalk of image pixel array. Based on these measurement properties, the proposed AMCW ToF scanning sensor shows highly precise 3D depth measurement performance. In this study, this precise measurement performance is explained in detail. Additionally, the actual measurement performance of the proposed sensor platform is experimentally validated under various conditions.

eess.SP↗