Indoor wireless ranging localization is a promising approach for low-power and high-accuracy localization of wearable devices. A primary challenge in this domain stems from non-line of sight propagation of radio waves. This study tackles a fundamental issue in wireless ranging: the unpredictability of real-time multipath determination, especially in challenging conditions such as when there is no direct line of sight. We achieve this by fusing range measurements with inertial measurements obtained from a low cost Inertial Measurement Unit (IMU). For this purpose, we introduce a novel asymmetric noise model crafted specifically for non-Gaussian multipath disturbances. Additionally, we present a novel Levenberg-Marquardt (LM)-family trust-region adaptation of the iSAM2 fusion algorithm, which is optimized for robust performance for our ranging-IMU fusion problem. We evaluate our solution in a densely occupied real office environment. Our proposed solution can achieve temporally consistent localization with an average absolute accuracy of ∼0.3m in real-world settings. Furthermore, our results indicate that we can achieve comparable accuracy even with infrequent range measurements down to 1Hz.

This paper proposes the inertial localization problem, the task of estimating the absolute location from a sequence of inertial sensor measurements. This is an exciting and unexplored area of indoor localization research, where we present a rich dataset with 53 hours of inertial sensor data and the associated ground truth locations. We developed a solution, dubbed neural inertial localization (NILoc) which 1) uses a neural inertial navigation technique to turn inertial sensor history to a sequence of velocity vectors; then 2) employs a transformer-based neural architecture to find the device location from the sequence of velocities. We only use an IMU sensor, which is energy efficient and privacy preserving compared to WiFi, cameras, and other data sources. Our approach is significantly faster and achieves competitive results even compared with state-of-the-art methods that require a floorplan and run 20 to 30 times slower. We share our code, model and data at https://sachini.github.io/niloc.

We explore a new sensor suite to provide a precise and robust navigation information, primarily intended for pedestrian localisation. We use an IMU sensor augmented with an array of magnetometers, called MIMU (for Magneto-Inertial measurement Unit) hereafter, and a single central camera as the vision sensor. The MIMU sensor has been shown in previous work to significantly improve the inertial dead-reckoning for pedestrian, provided we can assume a stationary and non-uniform magnetic environment. Such conditions are encountered in particular in indoor environment, where visual methods can be challenged by textureless or low-light areas. Alternatively, visual methods are useful in outdoor trajectories, where MIMU often fails because of a uniform magnetic field. We build a fusion estimator based on an inverse square-root filter fed with the raw measurement from magnetometers, accelerometers, gyrometers and visual features extracted and tracked in the video. Real-data experiments show the benefits of the fusion : on the one hand, the added information from the MIMU allows to complement the vision/inertial system in case where the vision does not provide useful information during an extended period of time; on the other hand, vision does extend the operational domain of the navigation system compared to a pure MIMU solution, in particular for outdoor sections of the trajectory.

This paper aims to leverage magnetic information from a Magneto-Inertial Measurement Unit - an IMU sensor augmented with an array of magnetometers, called MIMU hereafter - in a vision/inertial navigation system (VINS). This ego-motion estimation problem is formulated as an optimization over a sliding window fusing data from the MIMU with features tracked in a monocular camera image stream. The novelty of our approach lies in the formulation of preintegrated magnetic measurements that are computed from successive measurements of the local variations of the magnetic field, in the line of the preintegration of IMU data introduced in [1]. The resulting magnetic error terms participate to the minimized cost function along with the classical reprojection and IMU error terms. Our experiments show the benefits of this fusion. On the one hand, the added magnetic information from the MIMU allows to complement the VINS in cases where vision does not provide useful information during an extended period of time; on the other hand, vision does extend the operational domain of the navigation system compared to a pure MIMU solution, in particular for the outdoor portions of the trajectory.

We present a novel visual-magneto-inertial system for pedestrian indoor navigation. It includes magnetic, inertial and depth sensors integrated into a device that can be hand held by a pedestrian. Our method builds upon a magneto-inertial tachymeter that is able to accurately reconstruct body speed in presence of magnetic gradient and a depth registration algorithm for computing inter-image movement. We propose a simple fusion strategy using the magneto-inertial tachymeter in the predition step and the depth image registration in the correction step. We demonstrate accurate reconstruction of indoor trajectories in infrastructureless environments, with a drift often below one percent of the length of the trajectory. We emphasize the benefit of the proposed hybrid system in situations where the 3D environment is too simple, making depth registration useless, or situations where the magnetic environment lead to failure of the magneto-inertial tachymetry.

We propose a real-time, direct monocular SLAM method for omnidirectional or wide field-of-view fisheye cameras. Both tracking (direct image alignment) and mapping (pixel-wise distance filtering) are directly formulated for the unified omnidirectional model, which can model central imaging devices with a field of view above 180°. This is in contrast to existing direct mono-SLAM approaches like DTAM or LSD-SLAM, which operate on rectified images, in practice limiting the field of view to around 130° diagonally. Not only does this allows to observe - and reconstruct - a larger portion of the surrounding environment, but it also makes the system more robust to degenerate (rotation-only) movement. The two main contribution are (1) the formulation of direct image alignment for the unified omnidirectional model, and (2) a fast yet accurate approach to incremental stereo directly on distorted images. We evaluated our framework on real-world sequences taken with a 185° fisheye lens, and compare it to a rectified and a piecewise rectified approach.