LiDAR-based simultaneous localization and mapping (SLAM) exhibits excellent performance in indoor environments and is used to various aspects of robotic applications. Due to the inevitable odometry drift, map inconsistency always exists in mapping process. This issue is often addressed by loop correction, however, most loop correction method build pose to pose constraint based on appearance similarity, which is not able to detect map inconsistency with small overlap area.We proposed a plane-based loop detection method that is able to establish long-term association between plane feature and correct the drift without the need to go back to the same position. First, we identify map inconsistency between submap and global map. Then, we estimate relative transformation with revisited plane features. Then we utilize pose graph optimization (PGO) to correct the trajectory. Our proposed method has shown outstanding results compared with other state-of-the-art loop detection methods.

As a promising indoor localization technology, WiFi fingerprint-based localization encounters many issues that need to be addressed urgently, such as high-overhead fingerprint map construction, device heterogeneity among either mobile devices or access points (APs), etc. In this article, we present CBWF: a lightweight circular boundary -based WiFi fingerprinting localization system that is able to provide low-overhead, device calibration-free accurate indoor localization. CBWF achieves this by dividing a localization area into multiple subregions, and then leveraging the relation between the received signal strength (RSS) vectors from two different APs as fingerprints for localization. The key idea behind CBWF is that a superior division mechanism is attained to divide the localization area. Specifically, we propose the circle boundary mechanism to better approximate the real boundary of subregions, compared with the widely used linear boundary mechanism, and then sufficiently exploit the theoretical characteristics behind this novel mechanism. Extensive simulation and real-world experiments show that our lightweight system outperforms state-of-the-art approaches. Specifically, in a 40 m$\times 17$m real scenario with only 20 reference points (RPs) and 11 APs, CBWF achieves an average localization accuracy of 2.95 and 4.15 m for two different mobile devices, respectively. Our codes are available at:https://github.com/dadadaray/circular-boundary.

Vanishing point is an important geometric element in sports video. In this paper, a new calibration algorithm is proposed by using the algebraic and geometric properties of vanishing points, which resolves the three main problems of the traditional camera calibration technology based on vanishing points: (1) calibrating camera with varied focal length; (2) screening out outliers from a set of images; (3) estimation of distortion coefficients. The principal line passes through the principal point, and the algebraic relationship between it and the vanishing points is deduced. Using the geometry feature of the principal line, problems (1) and (2) can be easily solved. The linear relationship between the point and the line is used to estimate the distortion coefficient under the condition of obtaining the principal point. Simulation and real experiments show the validity and robustness of the proposed algorithm, and satisfactory results can be obtained by solving the above three problems.

WiFi fingerprinting-based indoor positioning system is vulnerable to the dynamic environment, which makes the positioning accuracy decrease and the fingerprint map invalid. To address these issues, an accurate indoor positioning system (AIPS) with fingerprint map adaptation is proposed. For online positioning, it treats the received signal strength (RSS) from each access point (AP) individually and can be divided into two steps: 1) coarse and 2) fine positioning. In coarse positioning, a novel clustering algorithm based on${RSS}$attenuation is proposed. In fine positioning, signal noise is considered to construct AP ring, and the reference point (RP) contained by the largest number of rings is selected as nearest RP, and then the RPs with larger number are searched out by region growing algorithm to estimate the location of test point (TP). For the fingerprint map adaptation,$K$-means is adopted to divide APs into two types, based on the number of rings, and to find which APs’ information has been changed by the dynamic environment, and then update them through Gaussian process regression. The experimental results show that the positioning algorithm in AIPS can improve the positioning accuracy compared with other algorithms, and the fingerprint map adaptation scheme in AIPS can reduce the online running time while keeping accuracy.

Pedestrian dead reckoning (PDR) is a promising complementary technique to balance the requirements on both accuracy and costs in outdoor and indoor positioning systems. In this paper, we propose a unified framework to comprehensively tackle the three sub problems involved in PDR, including step detection and counting, heading estimation and step length estimation, based on sequentially rotating the device (reference) frame to the Earth (reference) frame through sensor fusion. To be specific, a robust step detection and counting algorithm is devised according to vertical angular velocities and turns out to be tolerant of various smartphone placements; then, a zero velocity update (ZUPT) based algorithm is leveraged to calibrate the measurements in the Earth frame; on these grounds, the heading and step length are further estimated by exploiting the cyclic features of walking. A thorough and extensive experimental analysis is conducted and confirms the effectiveness and advantages of the proposed PDR framework as well as the corresponding algorithms.

This study proposes a novel shape matching algorithm through exploiting shape contexts. The contributions of the proposed algorithm are twofold: (i) a new framework is presented to deal with the shape matching problem based on shape contexts, but differently from existing methods, the authors exploit a polynomial fitting‐based feature point extraction method as a preprocessing step, so as to enhance the performance of the shape contexts‐based descriptor; (ii) the authors design a voting classification method based on the chi‐square statistical measure to evaluate the matching results. The experimental results show that this method is able to achieve high performance, even if shapes of testing objects suffer from translation, rotation and scaling.