Trajectory planning involving multi-agent interactions has been a long-standing challenge in the field of robotics, primarily burdened by the inherent yet intricate interactions among agents. While game-theoretic methods are widely acknowledged for their effectiveness in managing multi-agent interactions, significant impediments persist when it comes to accommodating the intentional uncertainties of agents. In the context of intentional uncertainties, the heavy computational burdens associated with existing game-theoretic methods are induced, leading to inefficiencies and poor scalability. In this paper, we propose a novel game-theoretic interactive trajectory planning method to effectively address the intentional uncertainties of agents, and it demonstrates both high efficiency and enhanced scalability. As the underpinning basis, we model the interactions between agents under intentional uncertainties as a static Bayesian game, and we show that its agent-form equivalence can be represented as a potential game under certain assumptions. The existence and attainability of the optimal interactive trajectories are illustrated, as the corresponding static Bayesian Nash equilibrium can be attained by optimizing a unified optimization problem. Additionally, we present a distributed algorithm based on the dual consensus alternating direction method of multipliers (ADMM) tailored to the parallel solving of the problem, thereby significantly improving the scalability. The attendant outcomes from simulations and experiments demonstrate that the proposed method is effective across a range of scenarios characterized by general forms of intentional uncertainties. Its scalability surpasses that of existing centralized and decentralized baselines, allowing for real-time interactive trajectory planning in uncertain game settings. The source code will be available onhttps://github.com/zhuangdf/Potential-Bayesian-Game-release.

In this paper, we introduce GS-LIVM, a real-time photo-realistic LiDAR-Inertial-Visual mapping framework with Gaussian Splatting tailored for outdoor scenes. Compared to existing methods based on Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), our approach enables real-time photo-realistic mapping while ensuring high-quality image rendering in large-scale unbounded outdoor environments. In this work, Gaussian Process Regression (GPR) is employed to mitigate the issues resulting from sparse and unevenly distributed LiDAR observations. The voxel-based 3D Gaussians map representation facilitates real-time dense mapping in large outdoor environments with acceleration governed by custom CUDA kernels. Moreover, the overall framework is designed in a covariance-centered manner, where the estimated covariance is used to initialize the scale and rotation of 3D Gaussians, as well as update the parameters of the GPR. We evaluate our algorithm on several outdoor datasets, and the results demonstrate that our method achieves state-of-the-art performance in terms of mapping efficiency and rendering quality. The source code is available on GitHub.

Predicting the motion of traffic agents is a fundamental challenge in autonomous driving, essential for safe and efficient ego-vehicle planning. Traditional methods typically focus on marginal forecasting, where the trajectory of each agent is predicted separately, leading to inconsistencies in scene-level predictions. To address this issue, we propose a scene-consistency network, named ScNet, which jointly predicts the trajectories of multiple agents in a single feedforward pass, ensuring consistency across all predictions. Our method leverages dual independently initialized student models that interact through cross-network contrastive learning at the global feature level, enhancing robustness and scene consistency in the learned representations. To further improve scene coherence, we incorporate a scene-guided strategy that refines these representations. Additionally, we employ a lightweight, anchor-free decoder that generates predictions for all agents, aligning the forecasts with real-world dynamics. Experiments show significant improvements in multi-world prediction metrics across complex environments. Code and models will be publicly available.

LiDAR-based localization serves as a critical component in autonomous systems, yet existing approaches face persistent challenges in balancing repeatability, accuracy, and environmental adaptability. Traditional point cloud registration methods relying solely on offline maps often exhibit limited robustness against long-term environmental changes, leading to localization drift and reliability degradation in dynamic real-world scenarios. To address these challenges, this paper proposes DuLoc, a robust and accurate localization method that tightly couples LiDAR-inertial odometry with offline map-based localization, incorporating a constant-velocity motion model to mitigate outlier noise in real-world scenarios. Specifically, we develop a LiDAR-based localization framework that seamlessly integrates a prior global map with dynamic real-time local maps, enabling robust localization in unbounded and changing environments. Extensive real-world experiments in ultra unbounded port that involve 2,856 hours of operational data across 32 Intelligent Guided Vehicles (IGVs) are conducted and reported in this study. The results attained demonstrate that our system outperforms other state-of-the-art LiDAR localization systems in large-scale changing outdoor environments.

Structure from Motion (SfM) and visual localization in indoor texture-less scenes and industrial scenarios present prevalent yet challenging research topics. Existing SfM methods designed for natural scenes typically yield low accuracy or map-building failures due to insufficient robust feature extraction in such settings. Visual markers, with their artificially designed features, can effectively address these issues. Nonetheless, existing marker-assisted SfM methods encounter problems like slow running speed and difficulties in convergence; and also, they are governed by the strong assumption of unique marker size. In this paper, we propose a novel SfM framework that utilizes planar markers and multiple cameras with known extrinsics to capture the surrounding environment and reconstruct the marker map. In our algorithm, the initial poses of markers and cameras are calculated with Perspective-n-Points (PnP) in the front-end, while bundle adjustment methods customized for markers and camera groups are designed in the back-end to optimize the 6-DOF pose directly. Our algorithm facilitates the reconstruction of large scenes with different marker sizes, and its accuracy and speed of map building are shown to surpass existing methods. Our approach is suitable for a wide range of scenarios, including laboratories, basements, warehouses, and other industrial settings. Furthermore, we incorporate representative scenarios into simulations and also supply our datasets with pose labels to address the scarcity of quantitative ground-truth datasets in this research field. The datasets and source code are available on GitHub1.