Jonas Callmer

Accurate and reliable soldier and first responder indoor positioning: multisensor systems and cooperative localization

A robust, accurate positioning system with seamless outdoor and indoor coverage is a highly needed tool for increasing safety in emergency response and military urban operations. It must be lightweight, small, inexpensive, and power efficient, and still provide meter-level accuracy during extended operations. GPS receivers, inertial sensors, and local radio-based ranging are natural choices for a multisensor positioning system. Inertial navigation with foot-mounted sensors is suitable as the core system in GPS denied environments, since it can yield meter-level accuracies for a few minutes. However, there is still a need for additional supporting sensors to keep the accuracy at acceptable levels during the duration of typical soldier and first responder operations. Suitable aiding sensors are three-axis magnetometers, barometers, imaging sensors, Doppler radars, and ultrasonic sensors. Further more, cooperative positioning, where first responders exchange position and error estimates in conjunction with performing radio based ranging, is deemed a key technology. This article provides a survey on technologies and concepts for high accuracy soldier and first responder positioning systems, with an emphasis on indoor positioning.

Stabilizing cell phone video using inertial measurement sensors

We present a system that rectifies and stabilizes video sequences on mobile devices with rolling-shutter cameras. The system corrects for rolling-shutter distortions using measurements from accelerometer and gyroscope sensors, and a 3D rotational distortion model. In order to obtain a stabilized video, and at the same time keep most content in view, we propose an adaptive low-pass filter algorithm to obtain the output camera trajectory. The accuracy of the orientation estimates has been evaluated experimentally using ground truth data from a motion capture system. We have conducted a user study, where the output from our system, implemented in iOS, has been compared to that of three other applications, as well as to the uncorrected video. The study shows that users prefer our sensor-based system.

Learning to detect loop closure from range data

Despite significant developments in the Simultaneous Localisation and Mapping (SLAM) problem, loop closure detection is still challenging in large scale unstructured environments. Current solutions rely on heuristics that lack generalisation properties, in particular when range sensors are the only source of information about the robots surrounding environment. This paper presents a machine learning approach for the loop closure detection problem using range sensors. A binary classifier based on boosting is used to detect loop closures. The algorithm performs robustly, even under potential occlusions and significant changes in rotation and translation. We developed a number of features, extracted from range data, that are invariant to rotation. Additionally, we present a general framework for scan-matching SLAM in outdoor environments. Experimental results in large scale urban environments show the robustness of the approach, with a detection rate of 85% and a false alarm rate of only 1%. The proposed algorithm can be computed in real-time and achieves competitive performance with no manual specification of thresholds given the features.

Silent localization of underwater sensors using magnetometers

Sensor localization is a central problem for sensor networks. If the sensor positions are uncertain, the target tracking ability of the sensor network is reduced. Sensor localization in underwater environments is traditionally addressed using acoustic range measurements involving known anchor or surface nodes. We explore the usage of triaxial magnetometers and a friendly vessel with known magnetic dipole to silently localize the sensors. The ferromagnetic field created by the dipole is measured by the magnetometers and is used to localize the sensors. The trajectory of the vessel and the sensor positions are estimated simultaneously using an Extended Kalman Filter (EKF). Simulations show that the sensors can be accurately positioned using magnetometers.

Geo-referencing for UAV navigation using environmental classification

A UAV navigation system relying on GPS is vulnerable to signal failure, making a drift free backup system necessary. We introduce a vision based geo-referencing system that uses pre-existing maps to reduce the long term drift. The system classifies an image according to its environmental content and thereafter matches it to an environmentally classified map over the operational area. This map matching provides a measurement of the absolute location of the UAV, that can easily be incorporated into a sensor fusion framework. Experiments show that the geo-referencing system reduces the long term drift in UAV navigation, enhancing the ability of the UAV to navigate accurately over large areas without the use of GPS.

Magnetometers for tracking metallic targets

Starting from Maxwells equations, we derive a sensor model for three-axis magnetometers suitable for localization and tracking applications. The model depends on the relative position between the sensor and the target, and a physical magnetic multipole model of the target. Both point targets (far-field) and extended target (near-field) models are provided. The models are validated on data taken from various road vehicles. The suitability of magnetometers for tracking is analyzed in terms of local observability and Cramér Rao lower bound as a function of the sensor positions in a two sensor scenario. Results from field test data indicate excellent tracking of position and velocity of the target, as well as identification of the magnetic target model suitable for target classification.

Single target tracking using vector magnetometers

With the electromagnetic theory as basis, we present a sensor model for three-axis magnetometers suitable for localization and tracking applications. The model depends on a physical magnetic dipole model of the target and its relative position to the sensor. Furthermore, the dependency between the magnetic dipole and the target orientation has been modeled enabling tracking of a maneuvering target. Due to multi-modality, a bank of Extended Kalman Filters is proposed for tracking road vehicles. Results from field test data indicate excellent tracking of target position.

Probabilistic stand still detection using foot mounted IMU

We consider stand still detection for indoor localization based on observations from a foot-mounted inertial measurement unit (IMU). The main contribution is a statistical framework for stand-still detection, which is a fundamental step in zero velocity update (ZUPT) to reduce the drift from cubic to linear in time. First, the observations are transformed to a test statistic having non-central chi-square distribution during zero velocity. Second, a hidden Markov model is used to describe the mode switching between stand still, walking, running, crawling and other possible movements. The resulting algorithm computes the probability of being in each mode, and it is easily extendable to a dynamic navigation framework where map information can be included. Results of first mode probability estimation, second map matching without ZUPT and third step length estimation with ZUPT are provided.

Radar SLAM using visual features

A vessel navigating in a critical environment such as an archipelago requires very accurate movement estimates. Intentional or unintentional jamming makes GPS unreliable as the only source of information and an additional independent supporting navigation system should be used. In this paper, we suggest estimating the vessel movements using a sequence of radar images from the preexisting body-fixed radar. Island landmarks in the radar scans are tracked between multiple scans using visual features. This provides information not only about the position of the vessel but also of its course and velocity. We present here a navigation framework that requires no additional hardware than the already existing naval radar sensor. Experiments show that visual radar features can be used to accurately estimate the vessel trajectory over an extensive data set.