Artificial intelligence-enhanced autonomous unmanned systems, such as large-scale autonomous robot networks, are widely used in logistic and industrial applications. In this article, we address the integrated task assignment, path planning, and coordination problem applied for large-scale robot networks with the existence of uncertainties. In particular, a novel generalized conflict graph is designed which encodes the traveling time cost of the subsequent path planning result of each task-robot assignment and also includes the predicted path conflicts of each two assignments. An integrated optimization problem which aims to minimize the total traveling cost and potential path conflicts simultaneously is first formulated and then transformed into a linear programming instance to obtain the optimal solution. In particular, to satisfy the real-time requirement in large-scale systems, a greedy solution is presented which has the near-optimal performance but can decrease the computational complexity by orders of magnitude. The optimality, scalability, robustness, and efficiency of our approach are demonstrated by comprehensive comparisons with existing state-of-the-art approaches. Note to Practitioners—With the development of artificial intelligence techniques, large-scale autonomous robot networks are increasingly used in the logistic warehouses, unmanned container terminals, and intelligence transportation systems. This article considers the large-scale networks with hundreds or even thousands of unmanned robots which are implemented in lifelong transportation systems with uncertainties existed in practical execution process. Our main concept is to simultaneously minimize the total time cost of all the tasks and the potential motion conflicts among all the robots in the subsequent execution stage, thus alleviating robot congestions, balancing traffic distributions, increasing system efficiency, and improving the robustness and scalability. Lifelong simulations with thousand robots illustrate that our approach can reduce more than 30% of the time steps consumed for coordinating robot motion conflicts, and in the meantime, the throughput, and overall system efficiency are improved. However, simulation results show that the system improvement decreases in the presence of extreme high uncertainties (such as temporary motion and communication failures of the robot), due to the inaccurate conflict prediction in the integrated optimization stage. Our future work includes the deep-learning-based traffic evolution prediction and the online reallocation and planning in highly dynamic scenarios.

Manhattan and Atlanta worlds hold for the structured scenes with only vertical and horizontal dominant directions (DDs). To describe the scenes with additional sloping DDs, a mixture of independent Manhattan worlds seems plausible, but may lead to unaligned and unrelated DDs. By contrast, we propose a novel structural model called Hong Kong world. It is more general than Manhattan and Atlanta worlds since it can represent the environments with slopes, e.g., a city with hilly terrain, a house with sloping roof, and a loft apartment with staircase. Moreover, it is more compact and accurate than a mixture of independent Manhattan worlds by enforcing the orthogonality constraints between not only vertical and horizontal DDs, but also horizontal and sloping DDs. We further leverage the structural regularity of Hong Kong world for the line-based SLAM. Our SLAM method is reliable thanks to three technical novelties. First, we estimate DDs/vanishing points in Hong Kong world in a semi-searching way. We use a new consensus voting strategy for search, instead of traditional branch and bound. This method is the first one that can simultaneously determine the number of DDs, and achieve quasi-global optimality in terms of the number of inliers. Second, we compute the camera pose by exploiting the spatial relations between DDs in Hong Kong world. This method generates concise polynomials, and thus is more accurate and efficient than existing approaches designed for unstructured scenes. Third, we refine the estimated DDs in Hong Kong world by a novel filter-based method. Then we use these refined DDs to optimize the camera poses and 3D lines, leading to higher accuracy and robustness than existing optimization algorithms. In addition, we establish the first dataset of sequential images in Hong Kong world. Experiments showed that our approach outperforms state-of-the-art methods in terms of accuracy and/or efficiency.

Image lines projected from parallel 3D lines intersect at a common point called the vanishing point (VP). Manhattan world holds for the scenes with three orthogonal VPs. In Manhattan world, given several lines in a calibrated image, we aim to cluster them by three unknown-but-sought VPs. The VP estimation can be reformulated as computing the rotation between the Manhattan frame and camera frame. To estimate three degrees of freedom (DOF) of this rotation, state-of-the-art methods are based on either data sampling or parameter search. However, they fail to guarantee high accuracy and efficiency simultaneously. In contrast, we propose a set of approaches that hybridize these two strategies. We first constrain two or one DOF of the rotation by two or one sampled image line. Then we search for the remaining one or two DOF based on branch and bound. Our sampling accelerates our search by reducing the search space and simplifying the bound computation. Our search achieves quasi-global optimality. Specifically, it guarantees to retrieve the maximum number of inliers on the condition that two or one DOF is constrained. Our hybridization of two-line sampling and one-DOF search can estimate VPs in real time. Our hybridization of one-line sampling and two-DOF search can estimate VPs in near real time. Experiments on both synthetic and real-world datasets demonstrated that our approaches outperform state-of-the-art methods in terms of accuracy and/or efficiency.

Estimating the rigid transformation with 6 degrees of freedom based on a putative 3D correspondence set is a crucial procedure in point cloud registration. Existing correspondence identification methods usually lead to large outlier ratios (>95% is common), underscoring the significance of robust registration methods. Many researchers turn to parameter search-based strategies (e.g., Branch-and-Bround) for robust registration. Although related methods show high robustness, their efficiency is limited to the high-dimensional search space. This paper proposes a heuristics-guided parameter search strategy to accelerate the search while maintaining high robustness. We first sample some correspondences (i.e., heuristics) and then just need to sequentially search the feasible regions that make each sample an inlier. Our strategy largely reduces the search space and can guarantee accuracy with only a few inlier samples, therefore enjoying an excellent trade-off between efficiency and robustness. Since directly parameterizing the 6-dimensional nonlinear feasible region for efficient search is intractable, we construct a three-stage decomposition pipeline to reparameterize the feasible region, resulting in three lower-dimensional sub-problems that are easily solvable via our strategy. Besides reducing the searching dimension, our decomposition enables the leverage of 1-dimensional interval stabbing at all three stages for searching acceleration. Moreover, we propose a valid sampling strategy to guarantee our sampling effectiveness, and a compatibility verification setup to further accelerate our search. Extensive experiments on both simulated and real-world datasets demonstrate that our approach exhibits comparable robustness with state-of-the-art methods while achieving a significant efficiency boost.

Simultaneous localization and mapping (SLAM) has achieved impressive performance in static environments. However, SLAM in dynamic environments remains an open question. Many methods directly filter out dynamic objects, resulting in incomplete scene reconstruction and limited accuracy of camera localization. The other works express dynamic objects by point clouds, sparse joints, or coarse meshes, which fails to provide a photo-realistic representation. To overcome the above limitations, we propose a photo-realistic and geometry-aware RGB-D SLAM method based on Gaussian splatting. Our method is composed of three main modules to 1) map the dynamic foreground including non-rigid humans/quadrupeds and rigid items, 2) reconstruct the static background, and 3) localize the camera. To map the foreground, we focus on modeling the deformations and/or motions. We consider the shape priors of humans/quadrupeds and exploit the geometric and appearance constraints of dynamic Gaussians. For background mapping, we design an optimization strategy between neighboring local maps by integrating appearance constraint into geometric alignment. As to camera localization, we leverage both static background and dynamic foreground to increase the number of observations and introduce more constraints. We explore the geometric and appearance constraints by associating 3D Gaussians with 2D optical flows and pixel patches. Experiments on extensive realworld datasets demonstrate that our method outperforms stateof-the-art approaches in terms of camera localization and scene mapping.