egocentric view integration / map reconstruction (slam-style fusion)

This work addresses the challenge in conventional tightly coupled SLAM systems, where time handling, geometric association, estimator design, and map updating are highly interdependent, hindering independent optimization. To overcome this, the paper introduces FUSE, a novel framework that decouples core SLAM state estimation components into standardized interfaces for the first time, defining unified mechanisms for observation input, state propagation, update, and query. This modular design enables flexible component substitution. A LiDAR–IMU system built upon FUSE integrates high-frequency IMU propagation, LiDAR-triggered geometric updates, residual filtering, and degeneracy-aware correction. Evaluated on a 418-meter looped corridor sequence, it achieves an end-to-end trajectory error of 1.626 meters, representing a 7.9% reduction in relative error compared to the best-performing baseline, Faster-LIO.

Real-time robotic control demands causal pose estimation—relying solely on past and current observations—yet visual SLAM violates causality via non-causal loop closure optimization, and visual-inertial odometry (VIO) suffers from unbounded drift. This paper introduces the first causal visual-inertial localization framework: a tightly coupled multi-camera–multi-map architecture that achieves bounded drift through online map selection, cross-map constraint propagation, IMU preintegration, and joint keyframe feature optimization. We establish the first comprehensive evaluation framework for causal localization, including a formal causal error model and a real-time relocalization mechanism. Evaluated on a newly collected long-term campus dataset and public benchmarks, our method guarantees strictly bounded localization error, improves accuracy by 37%, and operates in real time. The system implementation and dataset are publicly released.

To address low visual information fusion efficiency and poor cross-camera feature consistency in tightly coupled odometry integrating multiple fisheye cameras, LiDAR, and IMU, this paper proposes a tightly coupled SLAM framework based on panoramic visual feature modeling. Our method introduces: (1) a unified panoramic feature model that maps multi-fisheye observations onto a conformal spherical manifold for cross-view feature normalization; (2) an online extrinsic parameter compensation mechanism to mitigate triangulation inconsistency caused by calibration errors; and (3) joint embedding of panoramic geometric constraints, multi-view reprojection residuals, and IMU preintegration terms within a factor graph optimization framework. Extensive experiments on EuRoC, KITTI, and a custom dataset demonstrate that our system achieves an average 12.7% improvement in localization accuracy over state-of-the-art methods, while exhibiting significantly enhanced robustness against motion blur and illumination variations.

This work addresses the challenge of achieving real-time, low-latency 3D Gaussian Splatting-based SLAM in large-scale real-world environments by proposing a tightly coupled LiDAR–inertial–visual (LIV) fusion framework. The system concurrently performs state estimation, Gaussian primitive initialization, and global optimization, and introduces a cascaded strategy that integrates feedforward predictions with voxel-based PCA geometric priors to enhance initialization quality. Notably, it is the first to directly employ Generalized Iterative Closest Point (GICP) on the optimized global Gaussian map for loop closure detection and pose graph optimization, substantially improving global consistency in large-scale scenes. Experiments demonstrate that the proposed system achieves an excellent balance among localization accuracy, rendering fidelity, and real-time performance on both public and self-collected large-scale outdoor datasets featuring loop closures.

To address the insufficient real-time performance of SLAM in dynamic scenes, this paper proposes the first incremental-optimization-oriented dynamic SLAM framework enabling joint online estimation of static backgrounds and dynamic objects. Our method introduces a lightweight factor graph structure and system architecture specifically designed for incremental solving, integrating dynamic object motion modeling, joint camera pose optimization, incremental smoothing mapping, and sparse structural analysis. The approach achieves significant efficiency gains without compromising accuracy: it matches or surpasses state-of-the-art methods in precision across multiple standard benchmarks while accelerating inference by 5×. To the best of our knowledge, this is the first dynamic SLAM solution that unifies high accuracy with real-time performance. Furthermore, the framework exhibits strong scalability and is suitable for online deployment in real-world environments.

This work addresses the challenge in graph-optimized LiDAR SLAM where high trajectory accuracy often coexists with poor geometric consistency in revisited areas, primarily due to missed loop closures and residual drift. To tackle this, the authors propose an information-aware odometry constraint combined with a retrospective loop-closure mechanism. Key innovations include an information matrix estimation grounded in geometric dependency, a hierarchical loop-closure module that decouples place recognition from registration, and a retrospective strategy to recover missed loop closures. Experimental results demonstrate that the proposed method achieves state-of-the-art trajectory accuracy across multiple datasets while significantly improving local map geometric consistency. Additionally, the paper introduces a dedicated evaluation protocol for assessing map consistency.

This work addresses the world-state inconsistency arising from the conventional separation of SLAM and navigation modules. We present the first unified end-to-end navigation framework that integrates SLAM as an intrinsic mechanism within long-horizon world modeling. By leveraging incremental state updates and backend error optimization, the proposed approach maintains a globally consistent world representation while jointly predicting visual, kinematic, and geometric information to enable closed-loop navigation. Experimental results demonstrate that this framework substantially improves navigation performance while preserving high-precision SLAM estimation.

This work addresses the high communication overhead and low data efficiency in multi-robot collaborative SLAM, which often stems from reliance on low-level feature matching. The authors propose a distributed SLAM framework based on scene graph matching that leverages RGB-LiDAR fused point clouds for semantic segmentation, extracting discrete objects and their boundaries to construct scene graphs. Notably, inter-robot loop closures are achieved solely by exchanging object labels and centroids, eliminating dependence on raw feature descriptors. Integrated with a multi-stage communication scheme and distributed optimization, the method significantly reduces communication load while preserving localization and mapping accuracy, as demonstrated in both simulated and real-world experiments with legged robots across indoor and outdoor environments.

针对多时段大规模LiDAR SLAM的一致性问题，提出Chain-SLAM方法，通过链式回环闭合机制和统一因子图优化，实现跨时段地图对齐与复用。

This work addresses the inefficiency and limited accuracy of existing monocular dense SLAM systems, where visual odometry and mapping are loosely coupled. To overcome these limitations, we propose a real-time system that tightly integrates direct visual odometry with Gaussian splatting-based scene representation through a novel bidirectional coupling mechanism. Within an Expectation-Maximization (EM) framework, our approach jointly optimizes depth estimation and reconstruction in an end-to-end manner, without additional computational overhead. Key innovations include the first-ever bidirectional coupling between odometry and mapping, a heuristic-free strategy for initializing Gaussian splats, and a keyframe-based pixel association scheme. The resulting system achieves state-of-the-art performance in tracking accuracy, geometric fidelity, and photometric reconstruction quality while maintaining real-time operation.