Localization of a Miniature Spherical Rolling Robot Using IMU, Odometry and UWB

2018 ◽  
Author(s):  
Van Duong Nguyen ◽  
Gim Song Soh ◽  
Shaohui Foong ◽  
Kristin Wood
Keyword(s):  
Inertial Measurement Unit ◽  
Indoor Localization ◽  
Spherical Body ◽  
Ultra Wideband ◽  
Measurement Unit ◽  
Spherical Robot ◽  
Intelligent Surveillance ◽  
Localization Performance ◽  
High Degree ◽  
Rolling Robot

Robots that rolls with a spherical body or spherical robots, exhibits a high degree of mobility and amazing recovery capability from collisions while traversing in the environment. However, the localization of spherical robots in a GPS-denied environment for Intelligent Surveillance and Reconnaissance (ISR) task is a challenging problem due to the complexity of its system dynamics and the limited available sensors technology to sense out of the spherical shell. In our prior work, a kinematic localization technique based on odometry and inertial measurement unit (IMU) sensing was proposed and implemented onto our miniature spherical robot Virgo, for pose estimation. However, it suffers from errors due to slippages during locomotion or as a result of the collision. In this paper, we present a solution to this problem by the inclusion of an additional ultra-wideband (UWB) sensor and fuse it with our kinematic pose estimator using Extended Kalman Filter for indoor localization. Experiments are conducted on a multi-waypoint trajectory to verify its validity and had shown to improve localization performance.

scholarly journals A Robust and Adaptive Complementary Kalman Filter Based on Mahalanobis Distance for Ultra Wideband/Inertial Measurement Unit Fusion Positioning

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3435 ◽  
Author(s):  
Xin Li ◽  
Yan Wang ◽  
Kourosh Khoshelham
Keyword(s):  
Kalman Filter ◽  
Mahalanobis Distance ◽  
Inertial Measurement Unit ◽  
Least Squares Method ◽  
Ultra Wideband ◽  
Measurement Unit ◽  
Observation Error ◽  
System State ◽  
Inertial Measurement ◽  
The Impact

Ultra wideband (UWB) has been a popular technology for indoor positioning due to its high accuracy. However, in many indoor application scenarios UWB measurements are influenced by outliers under non-line of sight (NLOS) conditions. To detect and eliminate outlying UWB observations, we propose a UWB/Inertial Measurement Unit (UWB/IMU) fusion filter based on a Complementary Kalman Filter to track the errors of position, velocity and direction. By using the least squares method, the positioning residual of the UWB observation is calculated, the robustness factor of the observation is determined, and an observation weight is dynamically set. When the robustness factor does not exceed a pre-defined threshold, the observed value is considered trusted, and adaptive filtering is used to track the system state, while the abnormity of system state, which might be caused by IMU data exceptions or unreasonable noise settings, is detected by using Mahalanobis distance from the observation to the prior distribution. When the robustness factor exceeds the threshold, the observed value is considered abnormal, and robust filtering is used, whereby the impact of UWB data exceptions on the positioning results is reduced by exploiting Mahalanobis distance. Experimental results show that the observation error can be effectively estimated, and the proposed algorithm can achieve an improved positioning accuracy when affected by outlying system states of different quantity as well as outlying observations of different proportion.

scholarly journals Improving Accuracy of the Alpha–Beta Filter Algorithm Using an ANN-Based Learning Mechanism in Indoor Navigation System

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3946 ◽  
Author(s):  
Faisal Jamil ◽  
Do Hyeun Kim
Keyword(s):  
Inertial Measurement Unit ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Indoor Navigation ◽  
Prediction Algorithm ◽  
Measurement Unit ◽  
Sensor Reading ◽  
Inertial Measurement ◽  
Learning Module ◽  
Alpha Beta

The navigation system has been around for the last several years. Recently, the emergence of miniaturized sensors has made it easy to navigate the object in an indoor environment. These sensors give away a great deal of information about the user (location, posture, communication patterns, etc.), which helps in capturing the user’s context. Such information can be utilized to create smarter apps from which the user can benefit. A challenging new area that is receiving a lot of attention is Indoor Localization, whereas interest in location-based services is also rising. While numerous inertial measurement unit-based indoor localization techniques have been proposed, these techniques have many shortcomings related to accuracy and consistency. In this article, we present a novel solution for improving the accuracy of indoor navigation using a learning to perdition model. The design system tracks the location of the object in an indoor environment where the global positioning system and other satellites will not work properly. Moreover, in order to improve the accuracy of indoor navigation, we proposed a learning to prediction model-based artificial neural network to improve the prediction accuracy of the prediction algorithm. For experimental analysis, we use the next generation inertial measurement unit (IMU) in order to acquired sensing data. The next generation IMU is a compact IMU and data acquisition platform that combines onboard triple-axis sensors like accelerometers, gyroscopes, and magnetometers. Furthermore, we consider a scenario where the prediction algorithm is used to predict the actual sensor reading from the noisy sensor reading. Additionally, we have developed an artificial neural network-based learning module to tune the parameter of alpha and beta in the alpha–beta filter algorithm to minimize the amount of error in the current sensor readings. In order to evaluate the accuracy of the system, we carried out a number of experiments through which we observed that the alpha–beta filter with a learning module performed better than the traditional alpha–beta filter algorithm in terms of RMSE.

scholarly journals An Adaptive UWB/MEMS-IMU Complementary Kalman Filter for Indoor Location in NLOS Environment

2019 ◽  
Vol 11 (22) ◽  
pp. 2628 ◽  
Author(s):  
Liu ◽  
Li ◽  
Wang ◽  
Zhang
Keyword(s):  
Kalman Filter ◽  
Covariance Matrix ◽  
Inertial Measurement Unit ◽  
Ultra Wideband ◽  
Measurement Unit ◽  
Indoor Location ◽  
Magnetometer Data ◽  
Noise Covariance ◽  
Mems Imu ◽  
Non Line Of Sight

High precision positioning of UWB (ultra-wideband) in NLOS (non-line-of-sight) environment is one of the hot issues in the direction of indoor positioning. In this paper, a method of using a complementary Kalman filter (CKF) to fuse and filter UWB and IMU (inertial measurement unit) data and track the errors of variables such as position, speed, and direction is presented. Based on the uncertainty of magnetometer and acceleration, the noise covariance matrix of magnetometer and accelerometer is calculated dynamically, and then the weight of magnetometer data is set adaptively to correct the directional error of gyroscope. Based on the uncertainty of UWB distance observations, the covariance matrix of UWB measurement noise is calculated dynamically, and then the weight of UWB data observations is set adaptively to correct the position error. The position, velocity and direction errors are corrected by the fusion of UWB and IMU. The experimental results show that the algorithm can reduce the gyroscope deviation with magnetic noise and motion noise, so that the orientation estimates can be improved, as well as the positioning accuracy can be increased with UWB ranging noise.

scholarly journals Robust and Efficient Indoor Localization Using Sparse Semantic Information from a Spherical Camera

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4128 ◽  
Author(s):  
Irem Uygur ◽  
Renato Miyagusuku ◽  
Sarthak Pathak ◽  
Alessandro Moro ◽  
Atsushi Yamashita ◽  
...  
Keyword(s):  
Inertial Measurement Unit ◽  
Semantic Information ◽  
Indoor Localization ◽  
Low Cost ◽  
Large Data ◽  
Point Clouds ◽  
Measurement Unit ◽  
Sensor Model ◽  
Localization Approach ◽  
Spherical Camera

Self-localization enables a system to navigate and interact with its environment. In this study, we propose a novel sparse semantic self-localization approach for robust and efficient indoor localization. “Sparse semantic” refers to the detection of sparsely distributed objects such as doors and windows. We use sparse semantic information to self-localize on a human-readable 2D annotated map in the sensor model. Thus, compared to previous works using point clouds or other dense and large data structures, our work uses a small amount of sparse semantic information, which efficiently reduces uncertainty in real-time localization. Unlike complex 3D constructions, the annotated map required by our method can be easily prepared by marking the approximate centers of the annotated objects on a 2D map. Our approach is robust to the partial obstruction of views and geometrical errors on the map. The localization is performed using low-cost lightweight sensors, an inertial measurement unit and a spherical camera. We conducted experiments to show the feasibility and robustness of our approach.

scholarly journals Stereo Visual Odometry for Indoor Localization of Ship Model

2020 ◽  
Vol 58 (1) ◽  
pp. 57-75
Author(s):  
Mario Kučić ◽  
Marko Valčić
Keyword(s):  
Autonomous Navigation ◽  
Inertial Measurement Unit ◽  
Indoor Localization ◽  
Low Cost ◽  
Visual Odometry ◽  
Measurement Unit ◽  
Ship Model ◽  
Open Sea ◽  
Autonomous Cars ◽  
Stereo Cameras

Typically, ships are designed for open sea navigation and thus research of autonomous ships is mostly done for that particular area. This paper explores the possibility of using low-cost sensors for localization inside the small navigation area. The localization system is based on the technology used for developing autonomous cars. The main part of the system is visual odometry using stereo cameras fused with Inertial Measurement Unit (IMU) data coupled with Kalman and particle filters to get decimetre level accuracy inside a basin for different surface conditions. The visual odometry uses cropped frames for stereo cameras and Good features to track algorithm for extracting features to get depths for each feature that is used for estimation of ship model movement. Experimental results showed that the proposed system could localize itself within a decimetre accuracy implying that there is a real possibility for ships in using visual odometry for autonomous navigation on narrow waterways, which can have a significant impact on future transportation.

scholarly journals Multi-Ray Modeling of Ultrasonic Sensors and Application for Micro-UAV Localization in Indoor Environments

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1770 ◽  
Author(s):  
Lingyu Yang ◽  
Xiaoke Feng ◽  
Jing Zhang ◽  
Xiangqian Shu
Keyword(s):  
Indoor Localization ◽  
Measurement Unit ◽  
Indoor Environments ◽  
Ultrasonic Sensors ◽  
Sensor Model ◽  
Air Vehicle ◽  
Measurement Function ◽  
Simulation And Experiment ◽  
Localization Performance ◽  
Localization Approach

Due to its payload, size and computational limits, localizing a micro air vehicle (MAV) using only its onboard sensors in an indoor environment is a challenging problem in practice. This paper introduces an indoor localization approach that relies on only the inertial measurement unit (IMU) and four ultrasonic sensors. Specifically, a novel multi-ray ultrasonic sensor model is proposed to provide a rapid and accurate approximation of the complex beam pattern of the ultrasonic sensors. A fast algorithm for calculating the Jacobian matrix of the measurement function is presented, and then an extended Kalman filter (EKF) is used to fuse the information from the ultrasonic sensors and the IMU. A test based on a MaxSonar MB1222 sensor demonstrates the accuracy of the model, and a simulation and experiment based on the T h a l e s I I MAV platform are conducted. The results indicate good localization performance and robustness against measurement noises.

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