Simultaneous localization and mapping (SLAM) methods based on an RGB-D camera have been studied and used in robot navigation and perception. So far, most such SLAM methods have been applied to a static environment. However, these methods are incapable of avoiding the drift errors caused by moving objects such as pedestrians, which limits their practical performance in real-world applications. In this paper, a new RGB-D SLAM with moving object detection for dynamic indoor scenes is proposed. The proposed detection method for moving objects is based on mathematical models and geometric constraints, and it can be incorporated into the SLAM process as a data filtering process. In order to verify the proposed method, we conducted sufficient experiments on the public TUM RGB-D dataset and a sequence image dataset from our Kinect V1 camera; both were acquired in common dynamic indoor scenes. The detailed experimental results of our improved RGB-D SLAM were summarized and demonstrate its effectiveness in dynamic indoor scenes.

Chang’e-5, China’s first unmanned lunar sample-return mission, was successfully landed in Northern Oceanus Procellarum on 1 December 2020. Determining the lander location precisely and timely is critical for both engineering operations and subsequent scientific research. Localization of the lander was performed using radio-tracking and image-based methods. The lander location was determined to be (51.92°W, 43.06°N) by both methods. Other localization results were compared for cross-validation. The localization results greatly contributed to the planning of the ascender lifting off from the lander and subsequent maneuvers, and they will contribute to scientific analysis of the returned samples and in situ acquired data.

In planetary rover missions, rover path planning is critical to ensure the safety and efficiency of the rover traverse and in situ explorations. In the Chang’e-4 (CE-4) mission, we have proposed and developed vision-based decision support methods comprising obstacle map generation and path evaluation for the rover. At each waypoint along the rover traverse, digital elevation model (DEM) is automatically generated, which is then used for obstacle map generation and path searching. For path evaluation, the searched path and the predicted wheel trajectories are projected onto the original images captured by different cameras to coincide with the real observation scenario. The proposed methods have been applied in the CE-4 mission to support teleoperation of the rover, and examples of multiple applications used in the mission are presented in this paper. Under the support of the proposed techniques, by the end of the 14th lunar day (3 February 2020), the rover has already travelled 367.25 meters on the far side of the Moon.

In the study of SLAM problem using an RGB-D camera, depth information and visual information as two types of primary measurement data are rarely tightly coupled during refinement of camera pose estimation. In this paper, a new method of RGB-D camera SLAM is proposed based on extended bundle adjustment with integrated 2D and 3D information on the basis of a new projection model. First, the geometric relationship between the image plane coordinates and the depth values is constructed through RGB-D camera calibration. Then, 2D and 3D feature points are automatically extracted and matched between consecutive frames to build a continuous image network. Finally, extended bundle adjustment based on the new projection model, which takes both image and depth measurements into consideration, is applied to the image network for high-precision pose estimation. Field experiments show that the proposed method has a notably better performance than the traditional method, and the experimental results demonstrate the effectiveness of the proposed method in improving localization accuracy.

In the study of indoor simultaneous localization and mapping (SLAM) problems using a stereo camera, two types of primary features—point and line segments—have been widely used to calculate the pose of the camera. However, many feature-based SLAM systems are not robust when the camera moves sharply or turns too quickly. In this paper, an improved indoor visual SLAM method to better utilize the advantages of point and line segment features and achieve robust results in difficult environments is proposed. First, point and line segment features are automatically extracted and matched to build two kinds of projection models. Subsequently, for the optimization problem of line segment features, we add minimization of angle observation in addition to the traditional re-projection error of endpoints. Finally, our model of motion estimation, which is adaptive to the motion state of the camera, is applied to build a new combinational Hessian matrix and gradient vector for iterated pose estimation. Furthermore, our proposal has been tested on EuRoC MAV datasets and sequence images captured with our stereo camera. The experimental results demonstrate the effectiveness of our improved point-line feature based visual SLAM method in improving localization accuracy when the camera moves with rapid rotation or violent fluctuation.

In recent years, low-cost and lightweight RGB and depth (RGB-D) sensors, such as Microsoft Kinect, have made available rich image and depth data, making them very popular in the field of simultaneous localization and mapping (SLAM), which has been increasingly used in robotics, self-driving vehicles, and augmented reality. The RGB-D SLAM constructs 3D environmental models of natural landscapes while simultaneously estimating camera poses. However, in highly variable illumination and motion blur environments, long-distance tracking can result in large cumulative errors and scale shifts. To address this problem in actual applications, in this study, we propose a novel multithreaded RGB-D SLAM framework that incorporates a highly accurate prior terrestrial Light Detection and Ranging (LiDAR) point cloud, which can mitigate cumulative errors and improve the system’s robustness in large-scale and challenging scenarios. First, we employed deep learning to achieve system automatic initialization and motion recovery when tracking is lost. Next, we used terrestrial LiDAR point cloud to obtain prior data of the landscape, and then we applied the point-to-surface inductively coupled plasma (ICP) iterative algorithm to realize accurate camera pose control from the previously obtained LiDAR point cloud data, and finally expanded its control range in the local map construction. Furthermore, an innovative double window segment-based map optimization method is proposed to ensure consistency, better real-time performance, and high accuracy of map construction. The proposed method was tested for long-distance tracking and closed-loop in two different large indoor scenarios. The experimental results indicated that the standard deviation of the 3D map construction is 10 cm in a mapping distance of 100 m, compared with the LiDAR ground truth. Further, the relative cumulative error of the camera in closed-loop experiments is 0.09%, which is twice less than that of the typical SLAM algorithm (3.4%). Therefore, the proposed method was demonstrated to be more robust than the ORB-SLAM2 algorithm in complex indoor environments.