In the last decade, the surge in global container port throughput has heightened the need for terminal efficiency. The loading process plays a crucial role in overall port performance. However, the unpredictable arrival of external trucks poses challenges for yard cranes in scheduling both internal loading tasks and external truck tasks simultaneously. Existing approaches on yard crane scheduling, considering uncertain arrivals, typically rely on prior knowledge, which often fails to fully capture the nature of the real-life uncertainties. In response, we propose an online scheduling approach guided by a two-stage decision model, eliminating the need for prior knowledge of uncertain arrival and has the ability to dynamically adapt to different scenarios. In the look-ahead stage, future tasks are filtered dynamically to eliminate undesired tasks, followed by a priority rule guided selection stage, where the task with the highest priority is selected. Genetic Programming (GP) is employed for automated evolution of priority rules without human intervention. Realistic experiments showcase the effectiveness of the proposed dynamic look-ahead method compared to static minimum and maximum look-ahead, as well as the superiority of GP-evolved priority rules compared to manually crafted priority rules in terms of both performance and simplicity. A comprehensive analysis of GP-evolved rules highlights GP's proficiency in problem understanding and rule extraction, comparable to human experts.

In the paper, we develop a relaxed second-order total generalized variation model on triangulated surfaces, which couples the first-order gradient operator and the weighted divergence operator. An iterative two-stage mesh denoising method is proposed with the relaxed model, which contains facet normal filtering based on the relaxed model and robust vertex updating. The nondifferentiable optimization problem is solved by an iterative algorithm based on variable splitting and augmented Lagrangian method. Our denoising method is discussed and compared to several state-of-the-art techniques in terms of reconstruction quality, quantitative comparison, and computational costs. Experiments indicate that our approach is comparable to state-of-the-art algorithms at reasonable costs. It can produce denoising results with more structures, alleviate the staircase effect (false edges), and prevent edge flips. The quantitative errors also verify that the newly proposed algorithm is robust numerically.

Dynamic objects in the environment, such as people and other agents, lead to challenges for existing simultaneous localization and mapping (SLAM) approaches. To deal with dynamic environments, computer vision researchers usually apply some learning-based object detectors to remove these dynamic objects. However, these object detectors are computationally too expensive for mobile robot on-board processing. In practical applications, these objects output noisy sounds that can be effectively detected by on-board sound source localization. The directional information of the sound source object can be efficiently obtained by direction of sound arrival (DoA) estimation, but the depth estimation is difficult. Therefore, in this paper, we propose a novel audio-visual fusion approach that fuses sound source direction into the RGB-D image and thus removes the effect of dynamic obstacles on the multi-robot SLAM system. Experimental results of multirobot SLAM in different dynamic environments show that the proposed method uses very small computational resources to obtain very stable self-localization results.

Dynamic environments that include unstructured moving objects pose a hard problem for Simultaneous Localization and Mapping (SLAM) performance. The motion of rigid objects can be typically tracked by exploiting their texture and geometric features. However, humans moving in the scene are often one of the most important, interactive targets – they are very hard to track and reconstruct robustly due to non-rigid shapes. In this work, we present a fast, learning-based human object detector to isolate the dynamic human objects and realise a real-time dense background reconstruction framework. We go further by estimating and reconstructing the human pose and shape. The final output environment maps not only provide the dense static backgrounds but also contain the dynamic human meshes and their trajectories. Our Dynamic SLAM system runs at around 26 frames per second (fps) on GPUs, while additionally turning on accurate human pose estimation can be executed at up to 10 fps.

Dynamic environments are challenging for visual SLAM since the moving objects occlude the static environment features and lead to wrong camera motion estimation. In this paper, we present a novel dense RGB-D SLAM solution that simultaneously accomplishes the dynamic/static segmentation and camera ego-motion estimation as well as the static background reconstructions. Our novelty is using optical flow residuals to highlight the dynamic semantics in the RGB-D point clouds and provide more accurate and efficient dynamic/static segmentation for camera tracking and background reconstruction. The dense reconstruction results on public datasets and real dynamic scenes indicate that the proposed approach achieved accurate and efficient performances in both dynamic and static environments compared to state-of-the-art approaches.

Mesh surface denoising is a fundamental problem in geometry processing. The main challenge is to remove noise while preserving sharp features (such as edges and corners) and preventing generating false edges. We propose in this paper to combine total variation (TV) and piecewise constant function space for variational mesh denoising. We first give definitions of piecewise constant function spaces and associated operators. A variational mesh denoising method will then be presented by combining TV and piecewise constant function space. It is proved that, the solution of the variational problem (the key part of the method) is in some sense continuously dependent on its parameter, indicating that the solution is robust to small perturbations of this parameter. To solve the variational problem, we propose an efficient iterative algorithm (with an additional algorithmic parameter) based on variable splitting and augmented Lagrangian method, each step of which has closed form solution. Our denoising method is discussed and compared to several typical existing methods in various aspects. Experimental results show that our method outperforms all the compared methods for both CAD and non-CAD meshes at reasonable costs. It can preserve different levels of features well, and prevent generating false edges in most cases, even with the parameters evaluated by our estimation formulae.