In this paper, we propose a novel and efficient grid-based RBPF-SLAM with inter-particle map shaping (IPMS). The proposed method aims at saving the computational memory in the grid-based RBPF-SLAM while maintaining the mapping accuracy. Unlike conventional RBPF-SLAM in which each particle has its own map of the whole environment, each particle has only a small map of the nearby environment called an individual map in the proposed method. Instead, the map of the remaining large environment is shared by the particles. The part shared by the particles is called a base map. If the individual small maps become reliable enough to trust, they are merged with the base map. To determine when and which part of an individual map should be merged with the base map, we propose two map sharing criteria (MSC). Finally, the proposed inter-particle map sharing (IPMS) RBPF-SLAM is applied to the real-world datasets and benchmark datasets. The experimental results show that our method outperforms conventional methods in terms of map accuracy vs. memory consumption.

In this article, a novel method for simultaneous localization and mapping (SLAM) named surface elements (surfel) point SLAM (SP-SLAM) is proposed using an RGB-D camera. The key idea of SP-SLAM is to use not only keypoints but also surfels as features to cope with both high texture and low texture environments. By decomposing a surface into a small number of surfels, the method can represent spacious environments using a relatively small amount of memory. To optimize the poses of points, surfels, and cameras altogether, new objective functions are proposed, and a new bundle adjustment using these objective functions is developed. The proposed SP-SLAM runs in real time on a central processing unit as in other feature-based visual SLAM methods but works better than them not only in high texture but also in low texture environments, overcoming well-known drawbacks of feature-based visual SLAM with degradation in low texture environments. The proposed method is applied to benchmark datasets and its effectiveness is demonstrated by comparing against those of previous methods in terms of localization accuracy.

In this work, we propose a new visual odometry(VO) system that exploits the dynamic parts of an image. The key idea of our method is to identify the dynamic parts by combining semantic segmentation and optical flow and to suppress the dynamic parts in the process of VO estimation. First, movable objects are detected using the semantic segmentation. If an object contains many pixels of inconsistent optical flow, the object is considered as dynamic and merged with other dynamic objects to create a dynamic mask. Next, the ego-motion of the camera is estimated by using only the remaining static parts of the image and suppressing its dynamic parts. Unlike the other popular deep learning-based VO approaches, our method uses geometric approach based on optimization to achieve high performance. That is, the ego-motion of the camera is obtained by optimizing the correspondences obtained using the dynamic mask and optical flow consistency. Finally, the proposed method is applied to the KITTI Odometry benchmark dataset, and its performance is compared with that of the previous VO methods. Our method achieves an average of 7.67(%) translation error and 2.186 rotation error(°/100m), which imply the performance improvement by 21% and 11% from the state-of-the-art baseline, DF-VO, respectively. In addition, our method yields the RPE (Relative Pose Error) of 0.186m, which is the performance improvement by 52% against ORB-SLAM2, a popular geometry-based method. The experimental results show that the proposed VO method reliably predicts excellent visual odometry in the presence of dynamic objects.

This paper introduces a novel feature-based Visual Simultaneous Localization and Mapping (v-SLAM), termed FP(Finite-Plane)-SLAM, using an RGB-D camera Specifically, the problem solved in this paper is to extract sparse features effectively from RGB-D images and build a graph consisting of poses and sparse features as accurately as possible in real time on CPU. Motivated by SP-SLAM (Surfel-Point SLAM) [1], FP-SLAM extract points and surfels from RGB-D images as sparse features and optimize a pose and feature graph jointly. Compared to SP-SLAM, however, two new residuals are proposed to optimize the graph and improved the accuracy in FP-SLAM. These new residuals exploit the relationships between surfels (SS) and between points and surfels (PS), capturing inter-feature relations. Incorporating these new residuals with those from SP-SLAM, a total of four residuals are employed to jointly optimize points, surfels, and camera poses. Additionally, to expedite processing, the Jacobian of the error function is directly implemented in the optimization instead of using the off-the-shelve derivative module to speed up the processing. Finally, the effectiveness of the proposed FP-SLAM is validated on benchmark datasets by comparing it against to previous methods in terms of localization accuracy.

We present a visual–inertial odometry (VIO) system that integrates a deep feature extraction and filtering strategy with optical flow to improve tracking robustness. While many traditional VIO methods rely on hand-crafted features, they often struggle to remain robust under challenging visual conditions, such as low texture, motion blur, or lighting variation. These methods tend to exhibit large performance variance across different environments, primarily due to the limited repeatability and adaptability of hand-crafted keypoints. In contrast, learning-based features offer richer representations and can generalize across diverse domains thanks to data-driven training. However, they often suffer from uneven spatial distribution and temporal instability, which can degrade tracking performance. To address these issues, we propose a hybrid front-end that combines a lightweight deep feature extractor with an image pyramid and grid-based keypoint sampling to enhance spatial diversity. Additionally, a forward–backward optical-flow-consistency check is applied to filter unstable keypoints. The system improves feature tracking stability by enforcing spatial and temporal consistency while maintaining real-time efficiency. Finally, the effectiveness of the proposed VIO system is validated on the EuRoC MAV benchmark, showing a 19.35% reduction in trajectory RMSE and improved consistency across multiple sequences compared with previous methods.

This paper presents a lightweight semantic front-end for improving registration-driven Gaussian RGB-D SLAM in dynamic scenes. In dynamic environments, dynamic objects significantly degrade the robustness of Gaussian-based RGB-D SLAM systems by introducing inconsistent geometric observations during registration and map fusion. To address this problem, we propose an input-level dynamic removal framework for Gaussian-based SLAM that integrates real-time instance segmentation with a registration-driven Gaussian mapping pipeline. To maintain real-time performance under limited computational resources, the segmentation module is executed periodically and the most recent mask is reused for intermediate frames. Experimental results on public RGB-D benchmarks and real-world indoor sequences show that the proposed method improves trajectory robustness in dynamic environments and reduces dynamic-region contamination compared with the baseline neural explicit SLAM pipeline.