Seabed residential stations are essential for the future exploration of the deep-sea exploration. Deep-sea carriers accompany transport equipment for underwater resident stations. To accommodate the low-energy consumption and low-cost nature of deep-sea carriers, an online vertical velocity estimation method based on depth data is proposed. This method serves as a cost-effective alternative to the expensive acoustic and inertial velocity measurement equipment conventionally used in underwater navigation systems. To meet the demand for low-cost online velocity measurements, a dynamic model was created for buoyancy-driven vehicles such as deep-sea carriers during unpowered ascent. Two distinct methods were used to process depth sensor data: one involves central differencing followed by low-pass filtering, and the other entails the differentiation of the data after fitting it to dynamic equations. This approach yields two different types of estimated velocities. A sliding window was defined and used the variance of the velocity and fitting coefficient within the window as indicators to assess the uncertainty of the velocity data. Based on this evaluation, an adaptive weight allocation rule for the fusion process was formulated. This approach allows us to derive the weighted estimated velocity. Through lake trials, the weighted estimated velocity was validated to closely match the ideal velocity data and showed a substantial reduction in velocity variance compared to the differenced estimated velocity that was not fused. This indicates that the fluctuations in the velocity data were significantly reduced. In addition, the integrated displacement showed an average absolute error of 0.1769 m compared with the measured displacement. This indicates that the weighted estimated velocity effectively reflects the true state of motion of the deep-sea carrier. Furthermore, when unexpected motion occurs in deep-sea carriers, the weighted estimated velocity can prevent falling into the "smoothness trap.” This demonstrated the robustness of the proposed algorithm. •Online vertical velocity estimation method based on depth data.•Estimated velocity closely approximates the actual velocity of the deep-sea carrier.•Optimal estimated velocity showed good smoothness and robustness.•Adaptive weight allocation based on the uncertainty metric.