ELY-SLAM: An efficient SLAM algorithm with lightweight YOLO-based collaborative perception

SLAM in [formula omitted] with the Combined Kalman-Information Filter

To address the problem that the current optimization-based visual-inertial SLAM algorithms have difficulty in achieving high accuracy, high robustness and high efficiency at the same time, an efficient and versatile SLAM algorithm (EV-VINS) based on the improvement of VINS-Mono is proposed in this paper. EV-VINS tightly coupling the binocular and inertial measurement unit is divided into decoupled front-end, which detects GFTT corners and performs optical flow tracking, and back-end, which mainly performs initialization, nonlinear optimization and outlier rejection. The dense map module is added for subsequent navigation functions. In particular, a feature classification strategy is proposed based on the analysis of the existing problems in the initialization, which not only greatly reduces the number of outliers during initialization, but also makes the points used for back-end optimization have good initial values. Therefore, the computational efficiency and the stability of the back-end optimization are significantly improved while ensuring the accuracy. The simulation experiments based on the EuRoC datasets and actual test results show that EV-VINS has better real-time performance while having the basically same location accuracy compared to the classical algorithms.

This study introduces a computationally efficient SLAM algorithm that can map the elevation changes in undulating terrain and simultaneously localize the vehicle's position relative to the map. The algorithm enables autonomous navigation in unknown environments without using position fixes from external telemetry systems such as GPS and LBL. In particular, this approach can benefit a variety of underwater vehicle applications and expand the utility of autonomous underwater vehicles. The terrain-based SLAM algorithm involves severe nonlinearity due to complicated elevation changes in natural terrain, which leads to a nonlinear estimation problem. This research focused on developing a computationally efficient algorithm. Thus, the EKF algorithm incorporating depth measurements from a simple acoustic altimeter is employed to keep the computational cost at minimum. The feasibility and validity of the proposed algorithm is demonstrated through numerical simulations.

In this paper, an improved algorithm for 3D SLAM is presented. A data dimensional reduction algorithm—Principal Component Analysis (PCA) is adopted, so as to speed up the rate of feature extraction from RGB-D images(640×480). To overcome the poor efficiency performance on the original RGB-D SLAM, a novel memory management algorithm and an incremental appearance-based loop closure detector are used to realize the 3D mapping and robot's trajectory estimation. In the back-end, the robot's trajectory and global map is optimized by g2o. The experiments demonstrate that the proposed method is faster and more effective than the original RGB-D SLAM.

An accurate and computationally efficient SLAM algorithm is vital for autonomous vehicles. Most modern SLAM systems use feature detection approaches to limit computational requirements. Feature detection through a 3D point cloud can be a computationally challenging task. In this paper, we propose a feature-based SLAM algorithm using 2D image projections of the 3D laser point cloud. We use a camera parameters matrix to rasterize the 3D point cloud to an image. Then ORB feature detector is applied to these images. The proposed method gives repeatable and stable features in a variety of environments. Based on such features, we can estimate the 6dof pose of the robot. For loop detection, we employ a 2-step approach, i.e., nearest key-frame detection and loop candidate verification by matching features extracted from rasterized LIDAR images. We evaluate the proposed system with implementation on the KITTI dataset. Through experimental results, we show that the algorithm presented in this paper can substantially reduce the computational cost of feature detection from the point cloud and the whole SLAM system while giving accurate results.

This work presents a new hybrid metric map representation (HYMM) that combines feature maps with other dense metric sensory information. The global feature map is partitioned into a set of connected local triangular regions (LTRs), which provide a reference for a detailed multi-dimensional description of the environment. The HYMM framework permits the combination of efficient feature-based SLAM algorithms for localisation with, for example, occupancy grid (OG) maps. This fusion of feature and grid maps has several complementary properties; for example, grid maps can assist data association and can facilitate the extraction and incorporation of new landmarks as they become identified from multiple vantage points. The representation presented here will allow the robot to perform DenseSLAM. DenseSLAM is the process of performing SLAM whilst obtaining a dense environment representation.

This paper presents localizer module for mobile robot that travels around indoor warehouse environments. Our module uses the only one sensor, a single camera looking up the ceiling. There is no efficient enough SLAM algorithm working on embedded system. The initial difficulty of vision based SLAM is computational complexity to acquire reliable feature on their algorithm. To reduce the computational complexity, we use the ceiling segmentation to extract line features of ceiling area. Line features are extracted from the boundaries between the ceiling and walls. Extended Kalman Filter is used to estimate the pose of a robot and build the ceiling map with line features. The experiment is practiced in our indoor test bed and the proposed algorithm is proved by the experimental results.

In this paper, we first prove an interesting result for point feature based SLAM. “When the covariance matrices of feature observation errors are isotropic, the robot poses and feature positions obtained in each Gauss-Newton iteration (when solving a reformulated least squares optimisation based SLAM) are independent of the feature positions in the previous step”. That is, even if we reset the feature positions to different random values before each iteration, the results after the iteration never change. Building on this finding, we propose an algorithm to solve the robot poses only (“localisation”) and show that the algorithm generates exactly the same robot poses in each iteration as the Gauss-Newton method (SLAM). The optimal feature positions can be easily recovered using a closed-form formula after the optimal robot poses are obtained. Similarly, when the covariance matrices of odometry translation errors are also isotropic, we can prove that the SLAM results are independent of both the feature positions and the robot positions. Thus, we can have an “rotation-only algorithm” which generates the same robot rotations as the full SLAM. Again, the optimal robot positions and the optimal feature positions can be computed from the obtained optimal robot rotations using a closed-form formula. We have used multiple 2D and 3D SLAM datasets to demonstrate our research findings. The video shows the interesting convergence results can be found at https://youtu.be/j1T8toqGtDE. We expect the findings in this paper can help SLAM researchers to further understand the special structure of the SLAM problems and to further develop more efficient and reliable SLAM algorithms.

In the proposed study, we describe an approach to improving the computational efficiency and robustness of visual SLAM algorithms on mobile robots with multiple cameras and limited computational power by implementing an intermediate layer between the cameras and the SLAM pipeline. In this layer, the images are classified using a ResNet18-based neural network regarding their applicability to the robot localization. The network is trained on a six-camera dataset collected in the campus of the Skolkovo Institute of Science and Technology (Skoltech). For training, we use the images and ORB features that were successfully matched with subsequent frame of the same camera ("good" keypoints or features). The results have shown that the network is able to accurately determine the optimal images for ORB-SLAM2, and implementing the proposed approach in the SLAM pipeline can help significantly increase the number of images the SLAM algorithm can localize on, and improve the overall robustness of visual SLAM. The experiments on operation time state that the proposed approach is at least 6 times faster compared to using ORB extractor and feature matcher when operated on CPU, and more than 30 times faster when run on GPU. The network evaluation has shown at least 90% accuracy in recognizing images with a big number of "good" ORB keypoints. The use of the proposed approach allowed to maintain a high number of features throughout the dataset by robustly switching from cameras with feature-poor streams.

An efficient and accurate visual SLAM loop closure detection algorithm

We present an improved version of the treemap SLAM algorithm which uses Cholesky factors for representing Gaussians and a hierarchical tree partitioning algorithm derived from the established Kernighan-Lin heuristic for graph bisection. We demonstrate the algorithm's efficiency by mapping a simulated building with 1032271 landmarks. In the end, we close a million-landmarks loop in 21 ms, providing an estimate for ap10000 selected landmarks close to the robot, or in 442 ms for computing a full estimate

Simultaneous localization and map building (SLAM) is a fundamental and complex problem in mobile robot research. In SLAM, Kalman-filter-like implementations are widely adopted to localize a mobile robot and build a map simultaneously and incrementally. However, this approach requires extensive computations of order O(N/sup 2/), where N is the total number of landmarks. To make the computations more manageable, we propose a logarithmic map partitioning algorithm that partitions the global map into one local region and several sub-maps. The size of each sub-map is based on its distance from the mobile robot, and in each sub-map, a centroid landmark is selected to represent all the landmarks in the sub-map for SLAM computations. With this logarithmic-map partitioning, it maintains correlation updates with each sub-map and provides an efficient suboptimal solution to the SLAM problem. The number of landmarks reduces from N to a logarithm-based function of N, and the computational requirement reduces from O(N/sup 3/) to O(N/sup 2/), where N/sub L/ is the number of local landmarks. Furthermore, utilizing the compressed extended Kalman filter, the real-time computational complexity reduces to O(N/sub L//sup 2/). Computer simulation results showed that the proposed algorithm is consistent and efficient for a large number of landmarks.

This paper presents a very efficient SLAM algorithm that works by hierarchically dividing the map into local regions and subregions. At each level of the hierarchy each region stores a matrix representing some of the landmarks contained in this region. For keeping the matrices small only those landmarks are represented being observable from outside the region. A measurement is integrated into a local subregion using O(k 2) computation time for k landmarks in a subregion. When the robot moves to a different subregion a global update is necessary requiring only O(k 3 log n) computation time for n overall landmarks. The algorithm is evaluated for map quality, storage space and computation time using simulated and real experiments in an office environment.

The recent advance in 3D measurement technology, especially 3D laser scanners and RGB-D sensors like Microsoft Kinect, has made 3D point clouds feasibly accessible on mobile robots. Together with efficient SLAM algorithms, it is now possible to generate 3D point clouds of large environments like whole buildings or even cities at high speed and low cost. The problem is that these point clouds are usually not a suitable representation for classic robotic tasks like localization or even more sophisticated problems like scene interpretation. This thesis presents methods to create polygonal environment representations that can be used for semantic mapping and object recognition.

In this paper, we propose a scheme of generating a hybrid visual map for SLAM. The hybrid visual map has two levels of map representations: 1) the absolute map representation of highly distinctive visual planes via EKFSLAM and 2) the relative map representation of dense visual features for each visual plane via sparse information filter update. The absolute map can maintain its global consistency by matching the visual plane, the group of visual features. It improves data association and reduces the number of landmarks against individual visual features. Moreover, the relative map can reconstruct a 3-D map of visual features efficiently without loosing dense visual information. The performance of the proposed method was verified by the experimental results of consistent hybrid visual maps in an indoor environment.