We propose a dense indirect visual odometry method taking as input externally estimated optical flow fields instead of hand-crafted feature correspondences. We define our problem as a probabilistic model and develop a generalized-EM formulation for the joint inference of camera motion, pixel depth, and motion-track confidence. Contrary to traditional methods assuming Gaussian-distributed observation errors, we supervise our inference framework under an (empirically validated) adaptive log-logistic distribution model. Moreover, the log-logistic residual model generalizes well to different state-of-the-art optical flow methods, making our approach modular and agnostic to the choice of optical flow estimators. Our method achieved top-ranking results on both TUM RGB-D and KITTI odometry benchmarks. Our open-sourced implementation 1 is inherently GPU-friendly with only linear computational and storage growth.

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Under the influence of climate change and human activities, the spatial and temporal distribution of river runoff has changed. The statistical characteristics of runoff such as mean, variance and extreme values have changed significantly. Hydrological stationarity has been broken, deepening the uncertainty of water resources and their utilization. Hydrological stationarity is a fundamental assumption of traditional water resources planning and management. The occurrence of non-stationarity will undoubtedly have an impact on the operation and overall benefits of reservoirs, and may even threaten the safety of reservoirs and water resources. There is uncertainty as to whether reservoirs can operate safely and still achieve their design benefits under the new runoff conditions. Therefore, it is important to carry out adaptive regulation of reservoirs in response to non-stationary runoff. Based on the multi-objective theory of large system, a multi-objective joint scheduling model of the terrace reservoir group is constructed for adaptive regulation simulation. A set of combination schemes based on optimal scheduling, flood resource utilization, water saving is constructed. The adaptive regulation is validated using a real-world example of the Xiluodu cascade and Three Gorges cascade reservoirs system in Yangtze River, China. The adaptive regulation processes are analyzed by simulation and the adaptive regulation effects are evaluated. The results show that the non-stationary runoff in upper Yangtze River has had an impact on the comprehensive benefits of large hydropower projects. The use of non-engineering measures to improve flood resource utilization, adjust upstream water use behavior and optimize reservoir scheduling are effective means to reduce the negative impact of non-stationary runoff on cascade reservoirs system.

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