Browsing by Author "Cortes Reina, Santiago"

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Deep learning based speed estimation for constraining strapdown inertial navigation on smartphones
(2018) Cortes Reina, Santiago; Solin, Arno; Kannala, Juho
A4 Artikkeli konferenssijulkaisussa
Strapdown inertial navigation systems are sensitive to the quality of the data provided by the accelerometer and gyroscope. Low-grade IMUs in handheld smart-devices pose a problem for inertial odometry on these devices. We propose a scheme for constraining the inertial odometry problem by complementing non-linear state estimation by a CNN-based deep-learning model for inferring the momentary speed based on a window of IMU samples. We show the feasibility of the model using a wide range of data from an iPhone, and present proof-of-concept results for how the model can be combined with an inertial navigation system for three-dimensional inertial navigation.
Inertial Odometry on Handheld Smartphones
(2018) Solin, Arno; Cortes Reina, Santiago; Rahtu, Esa; Kannala, Juho
A4 Artikkeli konferenssijulkaisussa
Building a complete inertial navigation system using the limited quality data provided by current smartphones has been regarded challenging, if not impossible. This paper shows that by careful crafting and accounting for the weak information in the sensor samples, smartphones are capable of pure inertial navigation. We present a probabilistic approach for orientation and use-case free inertial odometry, which is based on double-integrating rotated accelerations. The strength of the model is in learning additive and multiplicative IMU biases online. We are able to track the phone position, velocity, and pose in realtime and in a computationally lightweight fashion by solving the inference with an extended Kalman filter. The information fusion is completed with zero-velocity updates (if the phone remains stationary), altitude correction from barometric pressure readings (if available), and pseudo-updates constraining the momentary speed. We demonstrate our approach using an iPad and iPhone in several indoor dead-reckoning applications and in a measurement tool setup.
Models and Methods for Inertial and Visual Odometry
(2021) Cortes Reina, Santiago
School of Science | Doctoral dissertation (article-based)
Smartphones have become a part of everyday modern life. Their presence and capabilities have changed the way a lot of tasks are performed. Among them is navigation, how to find a way from one point to another. Satellite-based navigation allows for easy localization and path planning when traveling outdoors in the open. Another recent capability is augmented reality and local mapping. However, medium-scale indoor navigation and mapping have not been reliably solved. Challenges in these environments are characterized by an unreliable satellite signal and visual features which are not easily recognizable or not revisited. This thesis is focused on inertial and visual-inertial navigation for pedestrian dead reckoning. The systems, algorithms, datasets, and benchmarks proposed in this thesis are all designed with smartphones and pedestrians in mind. The central statement of the thesis is that careful consideration of uncertainty sources, the characteristics of pedestrian motion, and the quality of the available signals allows for a system that produces reliable odometry over medium-length sequences of pedestrian motion. Two sensor modalities are explored in this thesis, inertial and visual-inertial odometry. Pure inertial odometry relies on complementary signals to keep a reliable estimate of the pose. Multiple approaches to utilize these complementary signals are explored in this thesis. Visual-inertial odometry is approached with computational efficiency in mind. The measurements from the camera and the inertial sensors are integrated using a state space model driven by inertial odometry. This is to allow for efficient use of camera information and complementary online processing. This thesis also introduces a dataset which is a representative sample of the kind of use cases that are problematic for most available systems. This dataset is used in both modalities to train, evaluate and benchmark results in pedestrian dead reckoning. The main contributions of this thesis are the proposed algorithms and data. This thesis shows that multiple modalities of information and multiple approaches to state estimation can be used to complement each other. The complementary use of sequential Bayesian estimation and deep learning has been explored further by the community, and many opportunities to improve on it are still to be discovered.