Adaptive Window-Width Trapezoidal Centroid Algorithm for High-Precision Pulsed LiDAR Ranging and Intensity Imaging

Conventional full-waveform centroid algorithms for pulsed LiDAR rely on rectangular integration and fixed windows, causing timing errors for asymmetric echo pulses and poor adaptability to varying pulse shapes. This paper proposes an adaptive window-width trapezoidal centroid algorithm (AWTWCA) that replaces rectangular with trapezoidal integration and dynamically determines the optimal window based on the rising-edge half-amplitude interval. Simulations under diverse echo conditions demonstrate that AWTWCA consistently achieves lower mean absolute error than rectangular and fixed-window trapezoidal methods. Experiments on a MEMS LiDAR system further validate its robustness: timing errors remain below 0.2 ns (3 cm ranging) and the detection success rate exceeds 99% under low-reflectivity and APD saturation conditions, significantly outperforming fixed-window algorithms. The algorithm simultaneously generates range and intensity point clouds, enabling clear discrimination of targets with different reflectivities at the same distance. With computational complexity virtually identical to conventional methods, AWTWCA offers a hardware-friendly solution for high-precision LiDAR signal processing in complex environments.