Modern smartphones have all the sensing capabilities required for accurate and robust navigation and tracking. In specific environments some data streams may be absent, less reliable, or flat out wrong. In particular, the GNSS signal can become flawed or silent inside buildings or in streets with tall buildings. In this application paper, we aim to advance the current state-of-the-art in motion estimation using inertial measurements in combination with partial GNSS data on standard smartphones. We show how iterative estimation methods help refine the positioning path estimates in retrospective use cases that can cover both fixed-interval and fixed-lag scenarios. We compare estimation results provided by global iterated Kalman filtering methods to those of a visual-inertial tracking scheme (Apple ARKit). The practical applicability is demonstrated on real-world use cases on empirical data acquired from both smartphones and tablet devices.
In this paper, we take a general INS approach, without assuming legged or otherwise constrained motion, and compensate the limitations of low quality IMUs by fusing them with GNSS position fixes, which may be potentially sparse and infrequent containing large gaps in signal reception. As mentioned, there are relatively few general INS approaches for consumer-grade devices. We build upon the recent work @cite_13 , which shows relatively good path estimation results by utilizing online learning of sensor biases and manually provided loop closures or position fixes. We improve their approach in the following two ways which greatly increase the practical applicability in certain use cases: we utilize automatic GNSS based position measurements, which do not require additional manoeuvres or cooperation from the user; and we apply iterative path reconstruction methods, which provide improved accuracy in the presence of long interruptions in GNSS signal reception.
@cite_13
1920612112 2029841124 1989137771 2162136131
The majority of Intelligent Transportation System (ITS) applications require an estimate of position, often generated through the fusion of satellite based positioning (such as GPS) with on-board inertial systems. To make the position estimates consistent it is necessary to understand the noise distribution of the information used in the estimation algorithm. For GNSS position information the noise distribution is commonly approximated as zero mean with Gaussian distribution, with the standard deviation used as an algorithm tuning parameter. A major issue with satellite based positioning is the well known problem of multipath which can introduce a non-linear and non-Gaussian error distribution for the position estimate. This paper introduces a novel algorithm that compares the noise distribution of the GNSS information with the more consistent noise distribution of the local egocentric sensors to effectively reject GNSS data that is inconsistent. The results presented in this paper show how the gating of the GNSS information in a strong multipath environment can maintain consistency in the position filter and dramatically improve the position estimate. This is particularly important when sharing information from different vehicles as in the case of cooperative perception due to the requirement to align information from various sources. Next generation driver assistance systems require precise self localization. Common approaches using global navigation satellite systems (GNSSs) suffer from multipath and shadowing effects often rendering this solution insufficient. In urban environments this problem becomes even more pronounced. Herein we present a system for six degrees of freedom (DOF) ego localization using a mono camera and an inertial measurement unit (IMU). The camera image is processed to yield a rough position estimate using a previously computed landmark map. Thereafter IMU measurements are fused with the position estimate for a refined localization update. Moreover, we present the mapping pipeline required for the creation of landmark maps. Finally, we present experiments on real world data. The accuracy of the system is evaluated by computing two independent ego positions of the same trajectory from two distinct cameras and investigating these estimates for consistency. A mean localization accuracy of 10 cm is achieved on a 10 km sequence in an inner city scenario. Global Navigation Satellite Systems (GNSS) can be used for navigation purposes in vehicular environments. However, the limited accuracy of GNSS makes it unsuitable for applications such as vehicle collision avoidance. Improving the positioning accuracy in vehicular networks, Cooperative Positioning (CP) algorithms have emerged. CP algorithms are based on data communication among vehicles and estimation of the distance between the nodes of the network. Among the variety of radio ranging techniques, Received Signal Strength (RSS) is very popular due to its simplicity and lower cost compared to other methods like Time of Arrival (TOA), and Time Difference of Arrival (TDOA). The main drawback of RSS- based ranging is its inaccuracy, which mostly originates from the uncertainty of the path loss exponent. Without knowing the environment path loss exponent, which is a time-varying parameter in the mobile networks, RSS is effectively useless for distance estimation. There are many approaches and techniques proposed in the literature for dynamic estimation of the path loss exponent within a certain environment. Most of these methods are not functional for mobile applications or their efficiency decreases dramatically with increasing mobility of the nodes. In this paper, we propose a method for dynamic estimation of the path loss exponent and distance based on the Doppler Effect and RSS. Since this method is fundamentally based on the Doppler Effect, it can be implemented within networks with mobile nodes. The higher the mobility of the nodes, the better performance of the proposed technique. This contribution is important because vehicles will be equipped with Dedicated Short Range Communication (DSRC) in the near future. A new scan that matches an aided Inertial Navigation System (INS) with a low-cost LiDAR is proposed as an alternative to GNSS-based navigation systems in GNSS-degraded or -denied environments such as indoor areas, dense forests, or urban canyons. In these areas, INS-based Dead Reckoning (DR) and Simultaneous Localization and Mapping (SLAM) technologies are normally used to estimate positions as separate tools. However, there are critical implementation problems with each standalone system. The drift errors of velocity, position, and heading angles in an INS will accumulate over time, and on-line calibration is a must for sustaining positioning accuracy. SLAM performance is poor in featureless environments where the matching errors can significantly increase. Each standalone positioning method cannot offer a sustainable navigation solution with acceptable accuracy. This paper integrates two complementary technologies—INS and LiDAR SLAM—into one navigation frame with a loosely coupled Extended Kalman Filter (EKF) to use the advantages and overcome the drawbacks of each system to establish a stable long-term navigation process. Static and dynamic field tests were carried out with a self-developed Unmanned Ground Vehicle (UGV) platform—NAVIS. The results prove that the proposed approach can provide positioning accuracy at the centimetre level for long-term operations, even in a featureless indoor environment.