The SPiForest, a new isolation-based approach to outlier detection, constructs iTrees on the space containing all attributes by probability density-based inverse sampling. Most existing iForest (iF)-based approaches can precisely and quickly detect outliers scattering around one or more normal clusters. However, the performance of these methods seriously decreases when facing outliers whose nature "few and different" disappears in subspace (e.g., anomalies surrounded by normal samples). To solve this problem, SPiForest is proposed, which is different from existing approaches. First, SPiForest uses the principal component analysis (PCA) to find principal components and estimate each component's probability density function (pdf). Second, SPiForest utilizes the inv-pdf, which is inversely proportional to the pdf estimated from the given dataset, to generate support points in the space containing all attributes. Third, the hyperplane decided by these support points is used to isolate the outliers in the space. Next, these steps are repeated to build an iTree. Finally, many iTrees construct a forest for outlier detection. SPiForest provides two benefits: 1) it isolates outliers with fewer hyperplanes, which significantly improves the accuracy and 2) it effectively detects the outliers whose nature "few and different" disappears in subspace. Comparative analyses and experiments show that the SPiForest achieves a significant improvement in terms of area under the curve (AUC) when compared with the state-of-the-art methods. Specifically, our method improves by at most 17.7% on AUC when compared to iF-based algorithms.

In recent years, indoor positioning has drawn intensive attention for both pedestrian and mobile robot applications. Among various indoor positioning technologies, visible light positioning has many advantages due to its high localization accuracy, high bandwidth, energy efficiency, long lifetime, and cost efficiency. For postprocessing or semi-real-time applications, researchers often use smoothers to improve location accuracy. However, smoothers are always local estimators and lack integrity when calculating locations. To globally optimize the positioning results and further improve the accuracy, we propose a nonlinear optimization model based on the idea of graph optimization. Innovatively, the model adds the acceleration as a constraint to become one part of the residuals and regularize the trajectory. We design a signal-to-noise ratio-based weighting strategy to suppress the outliers and better assess the errors. Moreover, we design a loop constraint to further improve the positioning accuracy. The experimental results show that our proposed model significantly improves the accuracy by 71%, which is suitable for indoor positioning.

Camera–inertial measurement unit (IMU) sensor fusion has been extensively studied in recent decades. Numerous observability analysis and fusion schemes for motion estimation with self-calibration have been presented. However, it has been uncertain whether the intrinsic parameters of both the camera and the IMU are observable under general motion. To answer this question, by using the Lie derivatives, we first prove that for a rolling shutter (RS) camera–IMU system, all the intrinsic and extrinsic parameters, camera time offset, and readout time of the RS camera are observable with an unknown landmark. To our knowledge, we are the first to present such a proof. Next, to validate this analysis and to solve the drift issue of a structureless filter during standstills, we develop a keyframe-based sliding window filter (KSWF) for odometry and self-calibration, which works with a monocular RS camera or stereo RS cameras. Though the keyframe concept is widely used in vision-based sensor fusion, to our knowledge, the KSWF is the first of its kind to support self-calibration. Our simulation and real data tests have validated that it is possible to fully calibrate the camera–IMU system using the observations of opportunistic landmarks under diverse motion. Real data tests confirmed previous allusions that keeping landmarks in the state vector can remedy the drift in standstill and showed that the keyframe-based scheme is an alternative solution.