slam

To address the high uncertainty in monocular visual localization and mapping during spacecraft proximity operations, this paper proposes an autonomous environmental modeling and self-localization method integrating active perception with factor graph smoothing-based SLAM. We introduce information-entropy-driven active perception into the spacecraft SLAM framework for the first time, enabling joint optimization of observation planning and state estimation through online camera pose optimization. The method models the joint posterior distribution of the state trajectory and map landmarks using a factor graph, and dynamically selects observation strategies that maximize information gain based on information-theoretic metrics. Numerical simulations demonstrate that, compared to passive perception, the proposed approach significantly improves pose and map accuracy, accelerating state uncertainty convergence by approximately 40%. These results validate the effectiveness and engineering applicability of the closed-loop “perception–planning–estimation” coordination mechanism.

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.

This work addresses the challenge of exploration and mapping decisions in active SLAM under partial observability by formulating it as a stochastic control problem with incomplete information. The authors propose a non-standard partially observable Markov decision process (POMDP) framework that jointly integrates motion, perception, and map representation. A key innovation is the introduction of an exploration cost function that explicitly captures the geometric structure of the state space to quantify the value of information-gathering actions. Building upon this formulation, they develop a general stochastic control model and derive an approximately optimal policy with theoretical guarantees by combining stochastic control theory and reinforcement learning algorithms. Numerical experiments in representative environments demonstrate the effectiveness of the approach, successfully learning high-performance exploration strategies.

This work addresses the challenges of fusing and synchronizing heterogeneous asynchronous sensors—such as rolling-shutter cameras, LiDAR, and event cameras—in continuous-time SLAM. The authors propose G-solver, a novel framework that uniquely integrates Gaussian belief propagation with a data-driven Gaussian process motion prior to enable accurate trajectory estimation without requiring hardware synchronization. At its core, G-solver models the trajectory continuously via a Gaussian process and leverages distributed Gaussian belief propagation for efficient inference, supporting temporal interpolation across sensor modalities and automatic hyperparameter learning. Experiments demonstrate that G-solver achieves accuracy and efficiency on par with state-of-the-art continuous-time SLAM methods on both synthetic and real-world datasets, while inherently supporting distributed optimization. The implementation is publicly available.

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.

This work addresses the critical challenge of jointly designing sensor query rates and noise covariance under resource and cost constraints to meet prescribed trajectory estimation accuracy requirements. It presents the first formalization of this problem as a unified optimization model, leveraging semidefinite programming (SDP) within the Kalman filter error covariance framework to simultaneously optimize measurement scheduling and noise parameters. The proposed approach efficiently determines whether a given accuracy target is achievable and, when feasible, synthesizes a corresponding implementation strategy. Experimental validation demonstrates that the computed sensor configurations consistently attain the desired accuracy in both simulated and real-world scenarios, while also reliably identifying infeasible accuracy demands.

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.

To address the challenge in dynamic environments where dense SLAM struggles to simultaneously achieve high-accuracy static mapping and faithful modeling of dynamic object motion, this paper proposes the first 4D Gaussian SLAM framework enabling joint high-fidelity reconstruction of static scenes and dynamic objects, along with robust camera tracking. Methodologically, it introduces a novel integration of geometry-guided dynamic segmentation, co-optimized static-dynamic 4D Gaussian representations, progressive pose refinement, and motion-consistency loss, further enhanced by multi-view geometric constraints and temporal motion modeling for joint optimization. Compared to prior approaches, our method preserves physically plausible trajectories of dynamic objects while significantly improving both camera tracking accuracy and scene reconstruction fidelity, achieving state-of-the-art performance across multiple dynamic SLAM benchmarks.

This work addresses the lack of a unified and transparent geometric modeling framework and rigorous mathematical derivation in existing tightly coupled LiDAR-inertial odometry systems. Within an iterative error-state Kalman filtering framework and leveraging a VoxelMap representation, the paper presents a self-contained, symbolically consistent tightly coupled fusion approach that explicitly unifies geometric constraints with probabilistic state estimation. By rigorously formulating the system model using Lie group and Lie algebra formalism, this study systematically clarifies and articulates the underlying principles for the first time, significantly enhancing the algorithm’s interpretability and reproducibility. The proposed methodology provides a foundational technical reference for the design and implementation of related LiDAR-inertial navigation systems.

This work addresses the challenges of trajectory drift accumulation and computationally expensive global consistency optimization in large-scale SLAM, as well as limitations of existing discrete grid-based submap stitching methods—such as discontinuous gradients and neglect of occupancy uncertainty—by introducing the first continuous probabilistic submap stitching framework. The method jointly optimizes submap poses and a global occupancy field in an implicit log-odds space, compressing raw observations into informative sufficient statistics via sparse Bayesian inference and incorporating a variance-weighting mechanism to preserve posterior uncertainty. It enables analytical Jacobian computation and directly yields an optimal global map with closed-form mean and variance upon pose convergence. Experiments demonstrate significant improvements over state-of-the-art approaches in both simulated and real large-scale environments, achieving higher pose accuracy, enhanced global consistency, greater map compactness, and better-calibrated uncertainty.