JungHwan Moon

Fixed angular separation between LiDAR (Light Detection and Ranging) channels results in different path lengths when the channels observe the same planar target; therefore, geometric effects must be considered when comparing inter-channel range measurements. This study investigated inter-channel range differences in a 2-channel LiDAR with fixed 0° and −3° viewing directions, focusing on short-term temporal dependence and temporal averaging. Repeated static measurements were conducted at 1∼5 m with five independent runs per distance. After the path-length difference expected from the nominal channel geometry was removed as a preprocessing step, the mean inter-channel residual ranged from −0.201 to 1.731 mm. The residual showed short-term positive temporal dependence, with a lag-1 autocorrelation of approximately 0.5 that attenuated toward zero within approximately 0.2∼0.3 s. Temporal averaging reduced residual variability more slowly than the $1/\sqrt{N}$ expectation for independent observations, while an autoregressive model of order 1 (AR(1))-based reference closely reproduced the empirical averaging behavior. These results demonstrate that short-term temporal dependence should be considered when evaluating precision improvement through temporal averaging of repeated inter-channel LiDAR measurements.

This paper experimentally analyzes how stepwise platform vibration (Baseline-S3, approximately 0.3–0.6 mm amplitude) alters the statistical structure of distance measurement errors in a dual-channel LIght Detection And Ranging (LiDAR) (0° and −3°) at a fixed horizontal distance of 1.5 m. The mean error remained at the ${10}^{-5}$ m level across all vibration stages, indicating negligible systematic bias. However, distribution-based metrics showed substantial amplification. The interquartile range (IQR) increased by approximately threefold from Baseline to S3, while the total error range expanded by roughly 4–11 times. The outlier ratio increased by about 1.5–2 times under high-vibration conditions. Both variance and root mean square error (RMSE) exhibited nonlinear growth with increasing vibration intensity. Two-way analysis of variance (ANOVA) revealed no statistically significant differences at the mean level ($p>0.05$), whereas variability-based indicators consistently demonstrated dispersion amplification. These findings indicate that LiDAR degradation under vibration is governed primarily by stochastic dispersion expansion and extreme-value behavior rather than systematic bias shift.