Yun Jiang

Light detection and ranging (LiDAR)–camera calibration requires reliable target reconstruction and robust pose estimation. We present P3LPnP, a plane-guided point-line framework that registers a known-size checkerboard lattice and jointly optimizes point re-projection and finite-segment length, direction, and endpoint position. The front end combines random sample consensus (RANSAC), density-based spatial clustering of applications with noise (DBSCAN), and principal component analysis (PCA). The back end uses iteratively re-weighted least squares with weights fixed during inner solves and exact-Huber step acceptance. In 30 matched synthetic scenes, P3LPnP achieved a plane root mean square error of 0.673 mm. Relative to point-only optimization, finite-segment constraints reduced mean clean-reference re-projection error from 0.441 to 0.399 pixels and translation error from 5.094 to 3.424 mm. Synthetic tests showed translation robustness to point noise and coherent bias, with competitive performance under sparse joint noise. A 30-run controlled replay from one system yielded mean re-projection errors of 0.356 and 0.338 pixels on 100 sparse reference points and 9900 same-session held-out points. Real-data evidence supported same-system consistency; the advantage over the point-line baseline was not statistically significant, and multi-system generalizability remained unverified.

When the Risley prism pair is used for target tracking, the nonlinear relationship between beam deflection and prism rotation makes tracking performance highly dependent on precise and stable motor control over a wide speed range. Although the brushless DC motor serves as the preferred drive source, its inherent commutation torque ripples directly induce beam pointing jitter, severely degrading overall tracking accuracy and stability. To address these issues, this paper proposes a three-vector-based model predictive direct speed control method. This approach establishes a direct speed-to-torque control channel by generating reference active power through dynamic equations, eliminating the need for fitting a constant flux linkage and parameter tuning. Simultaneously, combined with three-vector optimization and seven-segment modulation strategies, it achieves a dynamic balance between high-frequency, instantaneous electromagnetic power fine-tuning and inherent mechanical inertia of the rotor. Simulation results demonstrate that the proposed method exhibits superior speed stability compared to the conventional double-vector-based model predictive power control method and maintains high-precision dynamic tracking over a wide speed range. Ultimately, it leads to an average reduction of over 60% in the time-weighted absolute tracking error integral under various target trajectories, providing an effective solution for drive control of target tracking in Risley prism systems.

Single-photon lidar (SPL) exhibits high sensitivity, making it particularly suitable for detecting weak echoes over long distances. However, its susceptibility to background noise necessitates the implementation of advanced filtering techniques and complex algorithms, which can significantly increase system cost and complexity. To address these challenges, we propose a time-division-multiplexing-based correlated photon lidar system that employs a narrowband pulsed laser with stable time delays and variable pulse intensities, thereby establishing temporal and intensity correlations. This all-fiber solution not only simplifies the system architecture but also enhances operational efficiency. An adaptive cross-correlation method incorporating time slicing has been developed to extract histogram signals, enabling successful 1.5 km distance measurements under intense daytime noise conditions, using a 1 s accumulation time and a 20 mm receiving aperture. The experimental results demonstrate a 38% (from 1.11 to 1.52) improvement in the signal-to-noise ratio (SNR), thereby enhancing the system’s anti-noise capability, facilitating rapid detection, and reducing overall system costs.

Photon-counting LIDAR has effective ability to detect significantly weak echo. However, for imaging application, it typically needs a long acquisition time to obtain sufficient counts to reconstruct clear depth image, which obviously limits imaging efficiency and prevents its wide deployment. In this paper, we proposed a low acquisition time photon-counting imaging method based on two single element SPADs and pixel scanning structure. Compared to fast imaging with SPAD array, the method has low requirement on laser energy and can obtain depth image with arbitrary pixel resolution. Experiment result demonstrates its feasibility and a dynamic target can be clearly captured by the system with our specifically designed reconstruction algorithm. We also detailly investigated the working conditions for the method. It reveals more than 2 echo photons per pulse would be enough to reconstruct clear depth image even when noise is intensive. Restricted to our 10 KHz laser source, the result only exhibited 1 frame per second(fps) imaging with pixel resolution of 100*100. If a common 10 MHz laser is adopted, 512*512 pixels resolution images can be captured at the rate of nearly 40 fps.