Smartphone pedestrian navigation by foot-IMU sensor fusion

Gädeke, Tobias; Schmid, Johannes; Zahnlecker, Marc; Stork, Wilhelm; Müller-Glaser, Klaus D.

doi:10.1109/upinlbs.2012.6409787

Cited by 29 publications

(6 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To increase the motion tracking performance, an IMU sensor is also an integral part along with other types of sensors such as Zhao et al [ 17 ] who used an GPS/INS (inertial navigation system) system to correct positions when compared with the standalone INS; Gadeke at al. [ 18 ] proposed an IMU sensor fusion with a barometric sensor for tracking the position of smartphones in case of lost connection; Carlos A. et al [ 19 ] presented the motion tracking from human by using an IMU sensor, and a laser rangefinder to interact with robots; and Lake et al [ 20 ] established the interface device between an IMU sensor and EMG sensor to detect the motion from muscles. Moreover, an IMU sensor was successful in tracking accurately the movement by combining an accelerometer and a gyroscope, as shown in [ 21 , 22 ].…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Motion Estimation for Video Stabilization Based on an IMU Sensor

Auysakul

Pooneeth

2018

Sensors

View full text Add to dashboard Cite

Recorded video data must be clear for accuracy and faster analysis during post-processing, which often requires video stabilization systems to remove undesired motion. In this paper, we proposed a hybrid method to estimate the motion and to stabilize videos by the switching function. This method switched the estimated motion between a Kanade–Lucus–Tomasi (KLT) tracker and an IMU-aided motion estimator. It facilitated the best function to stabilize the video in real-time as those methods had numerous advantages in estimating the motion. To achieve this, we used a KLT tracker to correct the motion for low rotations and an IMU-aided motion estimator for high rotation, owing to the poor performance of the KLT tracker during larger movements. Furthermore, a Kalman filter was used to remove the undesired motion and hence smoothen the trajectory. To increase the frame rate, a multi-threaded approach was applied to execute the algorithm in the array. Irrespective of the situations exposed to the experimental results of the moving camera from five video sequences revealed that the proposed algorithm stabilized the video efficiently.

show abstract

Section: Related Workmentioning

confidence: 99%

A Hybrid Motion Estimation for Video Stabilization Based on an IMU Sensor

Auysakul

Pooneeth

2018

Sensors

View full text Add to dashboard Cite

show abstract

“…Inter-agent communication links are naturally wireless. However, the foot-mounting of the inertial sensors makes cabled connections impractical opting for battery powering and local wireless links for the sensors as well [55,56]. Unfortunately, the expensive filter updates, the wireless communication links, and the battery powering combines poorly with the required high sampling rates of the inertial sensors.…”

Section: A System Architecture and State Estimationmentioning

confidence: 99%

Cooperative localization by dual foot-mounted inertial sensors and inter-agent ranging

Nilsson

Zachariah

Skog

et al. 2013

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

The implementation challenges of cooperative localization by dual foot-mounted inertial sensors and inter-agent ranging are discussed, and work on the subject is reviewed. System architecture and sensor fusion are identified as key challenges. A partially decentralized system architecture based on step-wise inertial navigation and step-wise dead reckoning is presented. This architecture is argued to reduce the computational cost and required communication bandwidth by around two orders of magnitude while only giving negligible information loss in comparison with a naive centralized implementation. This makes a joint global state estimation feasible for up to a platoon-sized group of agents. Furthermore, robust and low-cost sensor fusion for the considered setup, based on state space transformation and marginalization, is presented. The transformation and marginalization are used to give the necessary flexibility for presented sampling-based updates for the inter-agent ranging and ranging free fusion of the two feet of an individual agent. Finally, the characteristics of the suggested implementation are demonstrated with simulations and a real-time system implementation.

show abstract

“…The foot-mounted approach has become a popular pedestrian navigation method, especially in applications with high accuracy demands, e.g., search and rescue, operations in industrial plants or navigation in complex buildings [15], [16]. The basic foot-mounted strapdown localization algorithm is depicted in Figure 3.…”

Section: A Foot-mounted Inertial Person Localizationmentioning

confidence: 99%

SmartLoCore: A Concept for an Adaptive Power-Aware Localization Processor

Tradowsky

Gädeke

Bruckschlogl

et al. 2014

2014 22nd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing

Self Cite

View full text Add to dashboard Cite

During the last decades, a great shift to processing on mobile and ubiquitous devices has been taken place. With this movement also the integration of different sensors, especially inertial sensors, has gained strong interest. One approach is to combine inertial sensors with other localization systems. However, this needs real-time processing capabilities and low power consumption, which is usually a problem when performing this task on the main processor with the operating system on top. In this paper, we present an adaptive localization processor called SmartLoCore including an intuitive development flow that allows hardware/software co-design trade-offs during design-time. These options include running the algorithm in software, adding new additional instructions to the core instruction-set and offloading complex mathematics onto dedicated hardware. We show the applicability with a complex localization application example and highlight the benefits of the proposed concept. We conclude that the SmartLoCore concept shows a very promising potential for a large variety of mobile systems.

show abstract

Smartphone pedestrian navigation by foot-IMU sensor fusion

Cited by 29 publications

References 17 publications

A Hybrid Motion Estimation for Video Stabilization Based on an IMU Sensor

A Hybrid Motion Estimation for Video Stabilization Based on an IMU Sensor

Cooperative localization by dual foot-mounted inertial sensors and inter-agent ranging

SmartLoCore: A Concept for an Adaptive Power-Aware Localization Processor

Contact Info

Product

Resources

About