The recently developed Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have shown encour-aging and impressive results for visual SLAM. However, most representative methods require RGBD sensors and are only available for indoor environments. The robustness of reconstruction in largescale outdoor scenarios remains unexplored. This paper introduces a large-scale 3DGS-based visual SLAM with stereo cameras, termed LSG-SLAM. The proposed LSG-SLAM employs a multi-modality strategy to estimate prior poses under large view changes. In tracking, we introduce feature-alignment warping constraints to alleviate the adverse effects of appearance similarity in rendering losses. For the scalability of large-scale scenarios, we introduce continuous Gaussian Splatting submaps to tackle unbounded scenes with limited memory. Loops are detected between GS sub maps by place recognition and the relative pose between looped keyframes is optimized utilizing rendering and feature warping losses. After the global optimization of camera poses and Gaussian points, a structure refinement module enhances the reconstruction quality. With extensive evaluations on the EuRoc and KITTI datasets, LSG-SLAM achieves superior performance over existing Neural, 3DGS-based, and even traditional approaches. Project page: https://lsg-slam.github.io.

Simultaneous Localization and Mapping (SLAM) is a fundamental task in robotics, driving numerous applications such as autonomous driving and virtual reality. Recent progress on neural implicit SLAM has shown encouraging and impressive results. However, the robustness of neural SLAM, particularly in challenging or data-limited situations, remains an unresolved issue. This paper presents HERO-SLAM, a Hybrid Enhanced Robust Optimization method for neural SLAM, which combines the benefits of neural implicit field and feature-metric optimization. This hybrid method optimizes a multi-resolution implicit field and enhances robustness in challenging environments with sudden viewpoint changes or sparse data collection. Our comprehensive experimental results on benchmarking datasets validate the effectiveness of our hybrid approach, demonstrating its superior performance over existing implicit field-based methods in challenging scenarios. HERO-SLAM provides a new pathway to enhance the stability, performance, and applicability of neural SLAM in real-world scenarios. Project page: https://hero-slam.github.io.

Abstract 3D Gaussian Splatting (3DGS) has emerged as a promising representation for scene reconstruction and novel view synthesis for its explicit representation and real‐time capabilities. This technique thus holds immense potential for use in mapping applications. Consequently, there is a growing need for an efficient and effective camera relocalization method to complement the advantages of 3DGS. This paper presents a camera relocalization method, namely GauLoc, in a scene represented by 3DGS. Unlike previous methods that rely on pose regression or photometric alignment, our proposed method leverages the differential rendering capability provided by 3DGS. The key insight of our work is the proposed implicit featuremetric alignment, which effectively optimizes the alignment between rendered keyframes and the query frames, and leverages the epipolar geometry to facilitate the convergence of camera poses conditioned explicit 3DGS representation. The proposed method significantly improves the relocalization accuracy even in complex scenarios with large initial camera rotation and translation deviations. Extensive experiments validate the effectiveness of our proposed method, showcasing its potential to be applied in many real‐world applications. Source code will be released at https://github.com/xinzhe11/GauLoc .

We address the problem of visual place recognition with perceptual changes. The fundamental problem of visual place recognition is generating robust image representations which are not only insensitive to environmental changes but also distinguishable to different places. Taking advantage of the feature extraction ability of Convolutional Neural Networks (CNNs), we further investigate how to localize discriminative visual landmarks that positively contribute to the similarity measurement, such as buildings and vegetations. In particular, a Landmark Localization Network (LLN) is designed to indicate which regions of an image are used for discrimination. Detailed experiments are conducted on open source datasets with varied appearance and viewpoint changes. The proposed approach achieves superior performance against state-of-the-art methods.

This paper proposes a visual localization system combining Convolutional Neural Networks (CNNs) and sparse point features to estimate the 6-DOF pose of the robot. The challenges of visual localization across time lie in that the same place captured across time appears dramatically different due to different illumination and weather conditions, viewpoint variations and dynamic objects. In this paper, a novel CNN-based place recognition approach is proposed, which requires no time-consuming feature generation process and no task-specific training. Moreover, we demonstrate that the rich semantic context information obtained from place recognition can greatly improve the subsequent feature matching process for pose estimation. The semantic constraint performs much better than traditional Bag-of-Words based methods for establishing correspondences between the query image and the map. To evaluate the robustness of the algorithm, the proposed system is integrated into ORB-SLAM2 and verified on the data collected over various illumination and weather conditions. Extensive experimental results show that even with weak ORB descriptors, the proposed system can significantly improve the success rate of localization under severe appearance changes.

Visual place recognition is the task of recognizing the query image in a set of dataset images. It is a challenging problem in computer vision due to frequent and unpredictable environmental changes. In this paper, a novel approach is proposed for visual place recognition. We consider the problem of visual place recognition as a probabilistic voting problem on coherent image sequences. According to the co-visibility relationship of images in the dataset, each query can be represented by a categorical variable. Therefore, the whole sequence is the distribution of several independent and non-identical categorical variables. Introducing the probabilistic framework not only removes the need for heuristic parameters but also recognizes location efficiently and effectively. Two widely used datasets are used to evaluate the performance of the proposed method. The probabilistic voting algorithm achieves superior performance compared with state-of-the-art methods and satisfies the real-time requirement.