To enhance the perception ability of snow field environments during nighttime and improve both the maintenance quality and operational efficiency of ski pistes, this paper proposes a novel segmentation algorithm based on time-domain waveform characteristics detection using vehicle-mounted LiDAR for the snow groomer and an improved simultaneous localization and mapping (SLAM) algorithm in ski pistes. First, based on a model of reflectivity distribution of the point cloud, snow noise points are identified and processed using linear interpolation to maintain the continuity of the time-domain waveform. Consequently, the grid is partitioned into specific scan perspectives to accurately estimate the real-time slope based on the elevation variation between neighboring grids. Then, a corresponding criterion for detecting step values in the time-domain waveforms is established to select the boundary points between pistes and obstacles according to the typical obstacles in alpine ski resorts. The segmentation can be achieved by enveloping the step values. Moreover, based on the segmentation results, features are classified and matched using feature constraint methods to improve the mapping speed. Finally, tests were conducted on the pistes of the Wanlong Ski Resort in Zhangjiakou. Experimental results show that the proposed algorithm for segmenting ski pistes achieves an average processing time of 2.36 ms per single-frame point cloud, while achieving an accuracy rate of over 98.54%. Moreover, when integrated into an improved SLAM approach with snow piste segmentation capabilities, it can achieve segmentation accurately while demonstrating superior mapping accuracy performance, as well as significantly reduced computational time.