Sensor Fusion Algorithm Research Articles

Design of Two-Wheeled Self-Balancing Robot Based on Sensor Fusion Algorithm

A two-wheeled, self-balancing robot is proposed using 6-axis MEMS sensors MPU6050 to measure its posture. The sensors integrated with a 3-axis gyroscope and a 3-axis accelerometer, can output the inclination of the robot based on sensor fusion algorithm. A handheld remote controller sends out commands to the robot such as forward, back, and turning around. According to the inclination and orientation commands, a 16-bit MCU using the PID control algorithm calculates the required control voltage for the motors, to adjust the robot’s posture and keep the body balanced. In this paper, the principle of the sensor fusion algorithm is fully described, and its effects are verified through related experiments. The experimental results show that the proposed robot is practical and able to balance using inexpensive MEMS sensors.

Bias Compensation of Gyroscopes in Mobiles with Optical Flow

In this paper a new technique is introduced for bias compensation of gyroscopes with a focus on mobiles phones. The standard problem of using gyroscopes is that integration of raw angular rates with non-zero bias will lead to continuous drift of the estimated orientation. To examine the nature of this bias, a simple error model was constructed for the whole device in terms of inertial sensing. For eliminating the bias, a sensor fusion algorithm was developed using the benefits of optical flow from the camera of the device. Our orientation estimator and bias removal method is based on complementary filters, in combination with an adaptive reliability filter for the optical flow features. The feedback of the fused result is combined with the raw gyroscope angular rates to compensate the bias. Various measurements were recorded on a real device running the demanding optical flow onboard. This way a robust and reliable fusion was constructed, which matched our expectations, and has been validated with simulations and real world measurements.

바람의 힘을 이용한 외바퀴 이동 로봇의 구현 및 균형제어

This paper presents the novel design, implementation and control of a single-wheel mobile robot that can balance by using air power from ducted fans. All of the motions of the single-wheel mobile robot are actuated by air power instead of motor torques. Using air power allows to reduce the total weight of the robot. The complementary sensor fusion algorithm is introduced to estimate the angle correctly. After several design and development, the robot is tested for balancing in the roll direction and yawing motion. In addition, the balancing control of the robot on a single rope is tested to evaluate the control performance.

Vehicle navigation algorithm based on unscented Kalman filter sensor information fusion

A new autonomous vehicle navigation model was proposed based on multi-sensor system for vehicle navigation and Global Positioning System(GPS) under complex road conditions. And the Unscented Kalman Filter(UKF) was used to overcome some security issues due to the sudden error produced by the Kalman filters with extensions,which belonged to Sigma point based sensor fusion algorithm. It is more suitable than the Kalman filters with extensions that the UKF can calculate the evaluation satisfied the requirement in vehicle navigation. Comparison experiments with the Kalman filter based on polynomial expansion were given in terms of estimation accuracy and computational speed. The experimental results show that the Sigma-point Kalman filter is a reliable and computationally efficient approach to state estimation-based control.Moreover,it is faster to evaluate the motion state of the vehicle according to the current direction situations and the feedback information of vehicle sensor,and can calculate the control input of vehicle adaptively in real time.

Multi-Sensor Fusion and Fault Detection using Hybrid Estimation for Air Traffic Surveillance

Data fusion for multiple surveillance sensors in air traffic control (ATC) is studied. The goal is to build up software redundancy for better target tracking accuracy and robustness against sensor faults. A set of hybrid estimation algorithms for different sensors is designed to run in parallel for tracking aircraft with changing flight modes. The proposed sensor fusion algorithm combines the estimates from each hybrid estimation algorithm and identifies potential sensor faults.

Comparative analysis of different adaptive filters for tracking lower segments of a human body using inertial motion sensors

For all segments and tests, a modified Kalman filter and a quasi-static sensor fusion algorithm were equally accurate (precision and accuracy ∼2–3°) compared to normalized least mean squares filtering, recursive least-squares filtering and standard Kalman filtering. The aims were to: (1) compare adaptive filtering techniques used for sensor fusion and (2) evaluate the precision and accuracy for a chosen adaptive filter. Motion sensors (based on inertial measurement units) are limited by accumulative integration errors arising from sensor bias. This drift can partly be handled with adaptive filtering techniques. To advance the measurement technique in this area, a new modified Kalman filter is developed. Differences in accuracy were observed during different tests especially drift in the internal/external rotation angle. This drift can be minimized if the sensors include magnetometers.

On Sensor Fusion for Airborne Wind Energy Systems

A study on filtering aspects of airborne wind energy generators is presented. This class of renewable energy systems aims to convert the aerodynamic forces generated by tethered wings, flying in closed paths transverse to the wind flow, into electricity. The accurate reconstruction of the wing's position, velocity and heading is of fundamental importance for the automatic control of these kinds of systems. The difficulty of the estimation problem arises from the nonlinear dynamics, wide speed range, large accelerations and fast changes of direction that the wing experiences during operation. It is shown that the overall nonlinear system has a specific structure allowing its partitioning into sub-systems, hence leading to a series of simpler filtering problems. Different sensor setups are then considered, and the related sensor fusion algorithms are presented. The results of experimental tests carried out with a small-scale prototype and wings of different sizes are discussed. The designed filtering algorithms rely purely on kinematic laws, hence they are independent from features like wing area, aerodynamic efficiency, mass, etc. Therefore, the presented results are representative also of systems with larger size and different wing design, different number of tethers and/or rigid wings.

Unscented Kalman Filter Based Sensor Fusion for Robust Optical and Electromagnetic Tracking in Surgical Navigation

Computer-aided surgery systems provide visual guidance to surgeons by showing the real-time pose of surgical instruments overlaid on preoperative medical images of the patient. Surgical instrument poses are localized in space using mainly optical tracking systems (OTS) and electromagnetic tracking systems (EMTS). OTS systems require clear line-of-sight, which is difficult to ensure in current overcrowded operating rooms. On the other hand, EMTSs provide less accuracy and suffer from magnetic field distortion in the presence of metal objects. In this paper, we propose a sensor fusion algorithm to compensate for the drawbacks of OTS and EMTS, and achieve robust tracking of surgical instruments. Spatial alignment of OTS and EMTS data will be achieved through a calibration procedure. The proposed sensor fusion algorithm uses an unscented Kalman filter (UKF), an extension of the standard Kalman filter based on a deterministic sampling of nonlinear functions. Quaternion representation for rotations is used to avoid singularities of other parameterizations (e.g., Euler angles). In cases of optical marker occlusion, our algorithm will take advantage of the EMTS to estimate the position of the hidden marker(s) and feed it in the UKF. The fusion algorithm will also keep track of an error matrix between OTS and EMTS measured poses, providing an up-to-date estimate of the electromagnetic distortion. This will be used to correct EMTS measurement before using it to compensate for possible optical line-of-sight occlusions. The proposed sensor fusion (SF) method is compared to a predictive UKF based on optical data (NSF). Our results show that the SF effectively compensates for short marker occlusion (nine samples) providing a continuous estimate of the instrument pose with an error significantly lower than the NSF method, and reaching a clinically acceptable accuracy. Furthermore, the proposed algorithm increases the accuracy of EMTS in the presence of magnetic field distortion.

Sensor Fusion with Low-Grade Inertial Sensors and Odometer to Estimate Geodetic Coordinates in Environments without GPS Signal

This paper presents a sensor fusion algorithm based on a Kalman Filter to estimate geodetic coordinates and reconstruct a car test trajectory in environments where there is no GPS signal. The sensor fusion algorithm is based on low-grade strapdown inertial sensors (i.e. accelerometers and gyroscopes) and an incremental odometer, from which, velocity measurements is obtained. Since the dynamic system is non linear, an Extended Kalman Filter (EKF) is used to estimate the states (i.e. latitude, longitude and altitude) and reconstruct the test trajectory. The relevance of this work is given by the fact that, in the current literature, much has been published about the merger Inertial Sensors and GPS, however, currently no literature that addresses the form of sensor fusion proposed here is available. Another aspect that could be emphasized is that the proposed algorithm has potential to be applied in environments where GPS signals are not available, such as Pipeline Inspection Gauge (PIG) as depicted below in figure 2. The inertial navigation system developed and tested, shows that only with inertial sensors measurements, a closed tested trajectory can not be reconstructed satisfactorily, however when it uses the sensor fusion, the trajectory can be reconstructed with relative success. On preliminary experiments, it was possible reconstruct a closed trajectory of approximately 2800m, attaining a final error of 13m.

Neural-network multiple models filter (NMM)-based position estimation system for autonomous vehicles

A highly accurate and reliable vehicle position estimation system is an important component of an autonomous driving system. In generally, a global positioning system (GPS) receiver is employed for the vehicle position estimation of autonomous vehicles. However, a stand-alone GPS does not always provide accurate and reliable information of the vehicle position due to frequent GPS blockages and multipath errors. In order to overcome these problems, a sensor fusion scheme that combines the data from the GPS receiver and several on-board sensors has been studied. In previous researches, a single model filter-based sensor fusion algorithm was used to integrate information from the GPS and on-board sensors. However, an estimate obtained from a single model is difficult to cover the various driving environments, including urban areas, off-road areas, and highways. Thus, a multiple models filter (MMF) has been introduced to address this limitation by adapting multiple models to a wide range of driving conditions. An adaptation of the multiple model is achieved through the use of the model probability. The MMF combines several vehicle models using the model probabilities, which indicate the suitability of the current driving condition. In this paper, we propose a vehicle position estimation algorithm for an autonomous vehicle that is based on a neural network (NN)-based MMF. The model probabilities are determined through the NN. The proposed position estimation system was evaluated through simulations and experiments. The experimental results show that the proposed position estimation algorithm is suitable for application in an autonomous driving system over a wide range of driving conditions.

Distributed Sensor Fusion for Scalar Field Mapping Using Mobile Sensor Networks

In this paper, autonomous mobile sensor networks are deployed to measure a scalar field and build its map. We develop a novel method for multiple mobile sensor nodes to build this map using noisy sensor measurements. Our method consists of two parts. First, we develop a distributed sensor fusion algorithm by integrating two different distributed consensus filters to achieve cooperative sensing among sensor nodes. This fusion algorithm has two phases. In the first phase, the weighted average consensus filter is developed, which allows each sensor node to find an estimate of the value of the scalar field at each time step. In the second phase, the average consensus filter is used to allow each sensor node to find a confidence of the estimate at each time step. The final estimate of the value of the scalar field is iteratively updated during the movement of the mobile sensors via weighted average. Second, we develop the distributed flocking-control algorithm to drive the mobile sensors to form a network and track the virtual leader moving along the field when only a small subset of the mobile sensors know the information of the leader. Experimental results are provided to demonstrate our proposed algorithms.

A Double-Stage Kalman Filter for Orientation Tracking With an Integrated Processor in 9-D IMU

This paper presents an application-specific integrated processor for an angular estimation system that works with 9-D inertial measurement units. The application-specific instruction-set processor (ASIP) was implemented on field-programmable gate array and interfaced with a gyro-plus-accelerometer 6-D sensor and with a magnetic compass. Output data were recorded on a personal computer and also used to perform a live demo. During system modeling and design, it was chosen to represent angular position data with a quaternion and to use an extended Kalman filter as sensor fusion algorithm. For this purpose, a novel two-stage filter was designed: The first stage uses accelerometer data, and the second one uses magnetic compass data for angular position correction. This allows flexibility, less computational requirements, and robustness to magnetic field anomalies. The final goal of this work is to realize an upgraded application-specified integrated circuit that controls the microelectromechanical systems (MEMS) sensor and integrates the ASIP. This will allow the MEMS sensor gyro plus accelerometer and the angular estimation system to be contained in a single package; this system might optionally work with an external magnetic compass.

Quasi-Real Time Estimation of Angular Kinematics Using Single-Axis Accelerometers

In human movement modeling, the problem of multi-link kinematics estimation by means of inertial measurement units has been investigated by several authors through efficient sensor fusion algorithms. In this perspective a single inertial measurement unit per link is required. This set-up is not cost-effective compared with a solution in which a single-axis accelerometer per link is used. In this paper, a novel fast technique is presented for the estimation of the sway angle in a multi-link chain by using a single-axis accelerometer per segment and by setting the boundary conditions through an ad hoc algorithm. The technique, based on the windowing of the accelerometer output, was firstly tested on a mechanical arm equipped with a single-axis accelerometer and a reference encoder. The technique is then tested on a subject performing a squat task for the knee flexion-extension angle evaluation by using two single-axis accelerometers placed on the thigh and shank segments, respectively. A stereo-photogrammetric system was used for validation. RMSEs (mean ± std) are 0.40 ± 0.02° (mean peak-to-peak range of 147.2 ± 4.9°) for the mechanical inverted pendulum and 1.01 ± 0.11° (mean peak-to-peak range of 59.29 ± 2.02°) for the knee flexion-extension angle. Results obtained in terms of RMSE were successfully compared with an Extended Kalman Filter applied to an inertial measurement unit. These results suggest the usability of the proposed algorithm in several fields, from automatic control to biomechanics, and open new opportunities to increase the accuracy of the existing tools for orientation evaluation.

Real-time Control System for Various Applications using Sensor Fusion Algorithm

Real-time control and monitoring systems uses sensors (accelerometers and gyroscopes), that can be applied to design inertial mouse, object tracking, motion sensing, stabilization and other systems. In order to acquire high system accuracy, most of the implementations demands huge real-time computing. Therefore, the prototype of real-time control system was designed using sensor fusion algorithm that ensures accurate various systems controls with minimal computing resources. Accelerometer data read and accelerometer with gyroscope fusion algorithm results were compared and simple 3D visualization was presented to verify and visually demonstrate the efficiency of proposed real-time control system prototype. DOI: http://dx.doi.org/10.5755/j01.eee.18.10.3064

GPS/INS센서 융합을 이용한 고 정밀 위치 추정에 관한 연구

There are several ways such as GPS(Global Positioning System) and INS (Inertial Navigation System) to track the location of moving vehicle. The GPS has the advantages of having non-accumulative error even if it brings about errors. In order to obtain the position information, we need to receive at least 3 satellites information. But, the weak point is that GPS is not useful when the 혠 signal is weak or it is in the incommunicable region such as tunnel. In the case of INS, the information of the position and posture of mobile with several Hz~several hundreds Hz data speed is recorded for velocity, direction. INS shows a very precise navigational performance for a short period, but it has the disadvantage of increasing velocity components because of the accumulated error during integration over time. In this paper, sensor fusion algorithm is applied to both of INS and GPS for the position information to overcome the drawbacks. The proposed system gets an accurate position information from experiment using SVD in a non-accessible GPS terrain.

Team AnnieWAY's Entry to the 2011 Grand Cooperative Driving Challenge

In this paper, we present the concepts and methods developed for the autonomous vehicle known as AnnieWAY, which is our winning entry to the 2011 Grand Cooperative Driving Challenge. We describe algorithms for sensor fusion, vehicle-to-vehicle communication, and cooperative control. Furthermore, we analyze the performance of the proposed methods and compare them with those of competing teams. We close with our results from the competition and lessons learned.

Magnetic perturbations detection and heading estimation using magnetometers

With the advent of smart phones equipped with Micro-Electro-Mechanical Systems, it has become possible to utilise one's orientation information along with location for Location-Based Services applications. Although gyroscopes are considered to be the primary sensors for orientation estimation, associated errors require periodic updates from other sources to mitigate them. Accelerometers can be used for estimating roll and pitch angles and magnetometers for estimating heading. However, the presence of man-made magnetic anomalies caused by electronic devices, ferrous materials, mechanical and electrical infrastructures contaminates the measurements since only the Earth's magnetic field is useful for estimating magnetically derived heading. Without proper magnetic disturbances detection and mitigation technique, heading estimates become erroneous and can corrupt the entire location estimation process. Because heading is the primary source of error in pedestrian navigation and the magnetic field is available everywhere, an exhaustive analysis of magnetic anomalies and their impact on heading estimation has been conducted using data collected in six different pedestrian environments. Based on the statistical outcomes, a magnitude- and angle-based detector of magnetic disturbances has been developed. This information is also used for estimating the accuracy of the magnetically derived heading, which can further assist sensor fusion algorithms. An experimental assessment of the proposed detector was conducted in a shopping mall. A theoretical analysis and experimental results show that the proposed detector is capable of identifying the effects of perturbations on the Earth's magnetic field, which provides users with a better estimate of magnetic heading in different pedestrian navigation environments.

Underwater Vehicle Localization with Complementary Filter: Performance Analysis in the Shallow Water Environment

Rapid development of underwater technology during the last two decades yielded more affordable sensors and underwater vehicles, and, as a result, expanded their use from exclusively offshore industry towards smaller interdisciplinary research groups. Regardless of application, knowing the location of the vehicle operating underwater is crucial. Relatively inexpensive solution is sensor fusion based on a dynamic model of the vehicle aided by a Doppler Velocity Log and a Ultra-Short Base Line position system. Raw data from the sensors are highly asynchronous and susceptible to outliers, especially in shallow water environment. This paper presents detailed sensor analysis based on experimental data gathered in shallow waters, identifies outliers, presents an intuitive and simple sensor fusion algorithm and finally, discusses outlier rejection. The approach has been experimentally verified on medium size remotely operated vehicle.

Toward Blind Travel Support through Verbal Route Directions: A Path Inference Algorithm for Inferring New Route Descriptions from Existing Route Descriptions

The work presented in this article continues our investigation of such assisted navigation solutions where the main emphasis is placed not on sensor sets or sensor fusion algorithms but on the ability of the travelers to interpret and contextualize verbal route directions en route. This work contributes to our investigation of the research hypothesis that we have formulated and partially validated in our previous studies: if a route is verbally described in sufficient and appropriate amount of detail, independent VI travelers can use their O&M and problem solving skills to successfully follow the route without any wearable sensors or sensors embedded in the environment. In this investigation, we temporarily put aside the issue of how VI and blind travelers successfully interpret route directions en route and tackle the question of how those route directions can be created, generated, and maintained by online communities. In particular, we focus on the automation of path inference and present an algorithm that may be used as part of the background computation of VGI sites to find new paths in the previous route directions written by online community members, generate new route descriptions from them, and post them for subsequent community editing.

Sensor fusion-based dynamic positioning of ships using Extended Kalman and Particle Filtering

SUMMARYThe paper examines the problem of dynamic ship positioning with the use of Kalman Filter- and Particle Filter-based sensor fusion algorithms. The proposed approach enables to estimate accurately the ship's state vector by fusing the vessel's position and heading measurements coming from on-board sensors together with distance measurements coming from sensors located at the coast (e.g. radar). The estimated state vector is used in turn, in a control loop, to regulate the horizontal position and heading of the vessel. The performance of dynamic positioning of the ship based on Kalman and Particle Filtering is evaluated through simulation experiments.