We report on a small-size light detection and ranging (LiDAR) sensor, which offers the possibility of being used in the field during oil spill incidents. In the present study, we develop an algorithm that can distinguish between seawater and oil through the use of a laser at 905 nm wavelength. We investigate the ability of the sensor to detect three different oil types (light crude, bunker A, and bunker C) through experiments and analyze the differences between the types and volumes of spilled oil (1, 5, 10, 15, 20, 25, 30, 35, 40, and 50 mL). The results showed that our algorithm for detecting oil spills over seawater was successful: the LiDAR sensor was able to detect different oil types and volumes. Spilled oil area coverage ranged by more than 50% of the detected area, and the viscosity of bunker C oil reached up to 73%. In addition, the experimental oil spills were mainly formed of oil films of 1 mm and 2 mm thicknesses, which confirmed geometrical properties. Follow-up research should further investigate the characteristics of oil slick thickness measured by the LiDAR system and undertake field tests to assess the feasibility of using the LiDAR system in pollution incidents.

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.

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.