scholarly journals A FEASIBILITY STUDY ON INCREMENTAL BUNDLE ADJUSTMENT WITH FISHEYE IMAGES AND LOW-COST SENSORS

2019 ◽  
Vol XLII-2/W18 ◽  
pp. 167-171
Author(s):  
A. M. G. Tommaselli ◽  
M. B. Campos ◽  
L. F. Castanheiro ◽  
E. Honkavaara
Keyword(s):  
Microelectromechanical Systems ◽  
Low Cost ◽  
Space Structure ◽  
Bundle Adjustment ◽  
Global Navigation Satellite Systems ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Positional Accuracy ◽  
Integrated Sensor ◽  
Sensor Orientation

Abstract. Low cost imaging and positioning sensors are opening new frontiers for applications in near real-time Photogrammetry. Omnidirectional cameras acquiring images with 360° coverage, when combined with information coming from GNSS (Global Navigation Satellite Systems) and IMU (Inertial Measurement Unit), can efficiently estimate orientation and object space structure. However, several challenges remain in the use of low-cost sensors and image observations acquired by sensors with non-perspective inner geometry. The accuracy of the measurement using low-cost sensors is affected by different sources of errors and sensor stability. Microelectromechanical systems (MEMS) present a large gap between predicted and actual accuracy. This work presents a study on the performance of an integrated sensor orientation approach to estimate sensor orientation and 3D sparse point cloud, using an incremental bundle adjustment strategy and data coming from a low-cost portable mobile terrestrial system composed by off-theshelf navigation systems and a poly-dioptric system (Ricoh Theta S). Experiments were performed in an outdoor area (sidewalk), achieving a trajectory positional accuracy of 0.33 m and a meter level 3D reconstruction.

scholarly journals PERFORMANCE ASSESSMENT OF INTEGRATED SENSOR ORIENTATION WITH A LOW-COST GNSS RECEIVER

2017 ◽  
Vol IV-2/W3 ◽  
pp. 75-80 ◽  
Author(s):  
M. Rehak ◽  
J. Skaloud
Keyword(s):  
Position Control ◽  
Low Cost ◽  
Satellite System ◽  
Bundle Adjustment ◽  
Single Frequency ◽  
Reference Trajectory ◽  
Integrated Sensor ◽  
Homogeneous Surface ◽  
Gnss Receiver ◽  
Sensor Orientation

Mapping with Micro Aerial Vehicles (MAVs whose weight does not exceed 5&amp;thinsp;kg) is gaining importance in applications such as corridor mapping, road and pipeline inspections, or mapping of large areas with homogeneous surface structure, e.g. forest or agricultural fields. In these challenging scenarios, integrated sensor orientation (ISO) improves effectiveness and accuracy. Furthermore, in block geometry configurations, this mode of operation allows mapping without ground control points (GCPs). Accurate camera positions are traditionally determined by carrier-phase GNSS (Global Navigation Satellite System) positioning. However, such mode of positioning has strong requirements on receiver’s and antenna’s performance. In this article, we present a mapping project in which we employ a single-frequency, low-cost (<&amp;thinsp;$100) GNSS receiver on a MAV. The performance of the low-cost receiver is assessed by comparing its trajectory with a reference trajectory obtained by a survey-grade, multi-frequency GNSS receiver. In addition, the camera positions derived from these two trajectories are used as observations in bundle adjustment (BA) projects and mapping accuracy is evaluated at check points (ChP). Several BA scenarios are considered with absolute and relative aerial position control. Additionally, the presented experiments show the possibility of BA to determine a camera-antenna spatial offset, so-called lever-arm.

scholarly journals Performance Assessment of an Ultra Low-Cost Inertial Measurement Unit for Ground Vehicle Navigation

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3865 ◽  
Author(s):  
Rodrigo Gonzalez ◽  
Paolo Dabove
Keyword(s):  
Automotive Industry ◽  
Inertial Measurement Unit ◽  
Microelectromechanical Systems ◽  
Inertial Sensor ◽  
Low Cost ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Mass Market ◽  
Inertial Measurement ◽  
Mems Imu

Nowadays, navigation systems are becoming common in the automotive industry due to advanced driver assistance systems and the development of autonomous vehicles. The MPU-6000 is a popular ultra low-cost Microelectromechanical Systems (MEMS) inertial measurement unit (IMU) used in several applications. Although this mass-market sensor is used extensively in a variety of fields, it has not caught the attention of the automotive industry. Moreover, a detailed performance analysis of this inertial sensor for ground navigation systems is not available in the previous literature. In this work, a deep examination of one MPU-6000 IMU as part of a low-cost navigation system for ground vehicles is provided. The steps to characterize the performance of the MPU-6000 are divided in two phases: static and kinematic analyses. Besides, an additional MEMS IMU of superior quality is also included in all experiments just for the purpose of comparison. After the static analysis, a kinematic test is conducted by generating a real urban trajectory registering an MPU-6000 IMU, the higher-grade MEMS IMU, and two GNSS receivers. The kinematic trajectory is divided in two parts, a normal trajectory with good satellites visibility and a second part where the Global Navigation Satellite System (GNSS) signal is forced to be lost. Evaluating the attitude and position inaccuracies from these two scenarios, it is concluded in this preliminary work that this mass-market IMU can be considered as a convenient inertial sensor for low-cost integrated navigation systems for applications that can tolerate a 3D position error of about 2 m and a heading angle error of about 3 °.

scholarly journals MEMS Based SINS/OD Filter for Land Vehicles’ Applications

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Huisheng Liu ◽  
Zengcai Wang ◽  
Susu Fang ◽  
Chao Li
Keyword(s):  
Navigation System ◽  
Velocity Measurement ◽  
Microelectromechanical Systems ◽  
Low Cost ◽  
Detection Algorithm ◽  
Measurement Unit ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Land Vehicles ◽  
Mems Imu

A constrained low-cost SINS/OD filter aided with magnetometer is proposed in this paper. The filter is designed to provide a land vehicle navigation solution by fusing the measurements of the microelectromechanical systems based inertial measurement unit (MEMS IMU), the magnetometer (MAG), and the velocity measurement from odometer (OD). First, accelerometer and magnetometer integrated algorithm is studied to stabilize the attitude angle. Next, a SINS/OD/MAG integrated navigation system is designed and simulated, using an adaptive Kalman filter (AKF). It is shown that the accuracy of the integrated navigation system will be implemented to some extent. The field-test shows that the azimuth misalignment angle will diminish to less than 1°. Finally, an outliers detection algorithm is studied to estimate the velocity measurement bias of the odometer. The experimental results show the enhancement in restraining observation outliers that improves the precision of the integrated navigation system.

scholarly journals UAV-LiCAM SYSTEM DEVELOPMENT: CALIBRATION AND GEO-REFERENCING

2018 ◽  
Vol XLII-1 ◽  
pp. 107-114 ◽  
Author(s):  
C. Cortes ◽  
M. Shahbazi ◽  
P. Ménard
Keyword(s):  
Particle Filtering ◽  
System Development ◽  
Low Cost ◽  
Base Station ◽  
Dual Frequency ◽  
Body Frame ◽  
Integrated Sensor ◽  
Sensor Orientation ◽  
Wide Range ◽  
Gnss Measurements

<p><strong>Abstract.</strong> In the last decade, applications of unmanned aerial vehicles (UAVs), as remote-sensing platforms, have extensively been investigated for fine-scale mapping, modeling and monitoring of the environment. In few recent years, integration of 3D laser scanners and cameras onboard UAVs has also received considerable attention as these two sensors provide complementary spatial/spectral information of the environment. Since lidar performs range and bearing measurements in its body-frame, precise GNSS/INS data are required to directly geo-reference the lidar measurements in an object-fixed coordinate system. However, such data comes at the price of tactical-grade inertial navigation sensors enabled with dual-frequency RTK-GNSS receivers, which also necessitates having access to a base station and proper post-processing software. Therefore, such UAV systems equipped with lidar and camera (UAV-LiCam Systems) are too expensive to be accessible to a wide range of users. Hence, new solutions must be developed to eliminate the need for costly navigation sensors. In this paper, a two-fold solution is proposed based on an in-house developed, low-cost system: 1) a multi-sensor self-calibration approach for calibrating the Li-Cam system based on planar and cylindrical multi-directional features; 2) an integrated sensor orientation method for georeferencing based on unscented particle filtering which compensates for time-variant IMU errors and eliminates the need for GNSS measurements.</p>

scholarly journals ASSESSING THE RELIABILITY AND THE ACCURACY OF ATTITUDE EXTRACTED FROM VISUAL ODOMETRY FOR LIDAR DATA GEOREFERENCING

2017 ◽  
Vol XLII-2/W6 ◽  
pp. 201-208
Author(s):  
B. Leroux ◽  
J. Cali ◽  
J. Verdun ◽  
L. Morel ◽  
H. He
Keyword(s):  
Test Bench ◽  
Low Cost ◽  
Control Point ◽  
Visual Odometry ◽  
Airborne Lidar ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Control Points ◽  
Ground Control Points ◽  
Orientation Parameters

Airborne LiDAR systems require the use of Direct Georeferencing (DG) in order to compute the coordinates of the surveyed point in the mapping frame. An UAV platform does not derogate to this need, but its payload has to be lighter than this installed onboard so the manufacturer needs to find an alternative to heavy sensors and navigation systems. For the georeferencing of these data, a possible solution could be to replace the Inertial Measurement Unit (IMU) by a camera and record the optical flow. The different frames would then be processed thanks to photogrammetry so as to extract the External Orientation Parameters (EOP) and, therefore, the path of the camera. The major advantages of this method called Visual Odometry (VO) is low cost, no drifts IMU-induced, option for the use of Ground Control Points (GCPs) such as on airborne photogrammetry surveys. In this paper we shall present a test bench designed to assess the reliability and accuracy of the attitude estimated from VO outputs. The test bench consists of a trolley which embeds a GNSS receiver, an IMU sensor and a camera. The LiDAR is replaced by a tacheometer in order to survey the control points already known. We have also developped a methodology applied to this test bench for the calibration of the external parameters and the computation of the surveyed point coordinates. Several tests have revealed a difference about 2&amp;ndash;3 centimeters between the control point coordinates measured and those already known.

scholarly journals Performance Characterization of GNSS/IMU/DVL Integration under Real Maritime Jamming Conditions

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2954 ◽  
Author(s):  
Ralf Ziebold ◽  
Daniel Medina ◽  
Michailas Romanovas ◽  
Christoph Lass ◽  
Stefan Gewies
Keyword(s):  
Inertial Sensors ◽  
Unscented Kalman Filter ◽  
Low Cost ◽  
Primary Source ◽  
Global Navigation Satellite Systems ◽  
Measurement Unit ◽  
Doppler Velocity ◽  
Satellite Systems ◽  
Tightly Coupled ◽  
The Baltic

Currently Global Navigation Satellite Systems (GNSSs) are the primary source for the determination of absolute position, navigation, and time (PNT) for merchant vessel navigation. Nevertheless, the performance of GNSSs can strongly degrade due to space weather events, jamming, and spoofing. Especially the increasing availability and adoption of low cost jammers lead to the question of how a continuous provision of PNT data can be realized in the vicinity of these devices. In general, three possible solutions for that challenge can be seen: (i) a jamming-resistant GNSS receiver; (ii) the usage of a terrestrial backup system; or (iii) the integration of GNSS with other onboard navigation sensors such as a speed log, a gyrocompass, and inertial sensors (inertial measurement unit—IMU). The present paper focuses on the third option by augmenting a classical IMU/GNSS sensor fusion scheme with a Doppler velocity log. Although the benefits of integrated IMU/GNSS navigation system have been already demonstrated for marine applications, a performance evaluation of such a multi-sensor system under real jamming conditions on a vessel seems to be still missing. The paper evaluates both loosely and tightly coupled fusion strategies implemented using an unscented Kalman filter (UKF). The performance of the proposed scheme is evaluated using the civilian maritime jamming testbed in the Baltic Sea.

scholarly journals A Novel Vehicle Stationary Detection Utilizing Map Matching and IMU Sensors

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Md. Syedul Amin ◽  
Mamun Bin Ibne Reaz ◽  
Salwa Sheikh Nasir ◽  
Mohammad Arif Sobhan Bhuiyan ◽  
Mohd. Alauddin Mohd. Ali
Keyword(s):  
Stationary State ◽  
Navigation System ◽  
Urban Areas ◽  
Microelectromechanical Systems ◽  
Low Cost ◽  
Measurement Unit ◽  
Map Matching ◽  
Mm Algorithm ◽  
Bias Drift ◽  
Navigation Information

Precise navigation is a vital need for many modern vehicular applications. The global positioning system (GPS) cannot provide continuous navigation information in urban areas. The widely used inertial navigation system (INS) can provide full vehicle state at high rates. However, the accuracy diverges quickly in low cost microelectromechanical systems (MEMS) based INS due to bias, drift, noise, and other errors. These errors can be corrected in a stationary state. But detecting stationary state is a challenging task. A novel stationary state detection technique from the variation of acceleration, heading, and pitch and roll of an attitude heading reference system (AHRS) built from the inertial measurement unit (IMU) sensors is proposed. Besides, the map matching (MM) algorithm detects the intersections where the vehicle is likely to stop. Combining these two results, the stationary state is detected with a smaller timing window of 3 s. A longer timing window of 5 s is used when the stationary state is detected only from the AHRS. The experimental results show that the stationary state is correctly identified and the position error is reduced to 90% and outperforms previously reported work. The proposed algorithm would help to reduce INS errors and enhance the performance of the navigation system.

scholarly journals A Low-Cost Method of Improving the GNSS/SINS Integrated Navigation System Using Multiple Receivers

Electronics ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 1079 ◽  
Author(s):  
Di Liu ◽  
Hengjun Wang ◽  
Qingyuan Xia ◽  
Changhui Jiang
Keyword(s):  
Navigation System ◽  
Low Cost ◽  
Field Tests ◽  
Measurement Unit ◽  
Filter Method ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Integration System ◽  
Integrated Navigation System ◽  
Multiple Receivers

GNSS (global navigation satellite system) and SINS (strap-down inertial navigation system) integrated navigation systems have been the apparatus for providing reliable and stable position and velocity information (PV). Commonly, there are two solutions to improve the GNSS/SINS integration navigation system accuracy, i.e., employing GNSS with higher position accuracy in the integration system or utilizing the high-grade inertial measurement unit (IMU) to construct the integration system. However, technologies such as RTK (real-time kinematic) and PPP (precise point positioning) that improve GNSS positioning accuracy have higher costs and they cannot work under high dynamic environments. Also, an IMU with high accuracy will lead to a higher cost and larger volume, therefore, a low-cost method to enhance the GNSS/SINS integration accuracy is of great significance. In this paper, multiple receivers based on the GNSS/SINS integrated navigation system are proposed with the aim of providing more precise PV information. Since the chip-scale receivers are cheap, the deployment of multiple receivers in the GNSS/SINS integration will not significantly increase the cost. In addition, two different filtering methods with central and cascaded structure are employed to process the multiple receivers and SINS integration. In the centralized integration filter method, measurements from multiple receivers are directly processed to estimate the SINS errors state vectors. However, the computation load increases heavily due to the rising dimension of the measurement vector. Therefore, a cascaded integration filter structure is also employed to distribute the processing of the multiple receiver and SINS integration. In the cascaded processing method, each receiver is regarded as an individual “sensor”, and a standard federated Kalman filter (FKF) is implemented to obtain an optimal estimation of the navigation solutions. In this paper, a simulation and a field tests are carried out to assess the influence of the number of receivers on the PV accuracy. A detailed analysis of these position and velocity results is presented and the improvements in the PV accuracy demonstrate the effectiveness of the proposed method.

Fast and robust visual odometry with a low-cost IMU in dynamic environments

2019 ◽  
Vol 46 (6) ◽  
pp. 882-894 ◽  
Author(s):  
Erliang Yao ◽  
Hexin Zhang ◽  
Haitao Song ◽  
Guoliang Zhang
Keyword(s):  
Indirect Method ◽  
Direct Method ◽  
Low Cost ◽  
Visual Odometry ◽  
Dynamic Environments ◽  
Motion Blur ◽  
Bundle Adjustment ◽  
Measurement Unit ◽  
Content Type ◽  
Camera Pose

Purpose To realize stable and precise localization in the dynamic environments, the authors propose a fast and robust visual odometry (VO) approach with a low-cost Inertial Measurement Unit (IMU) in this study. Design/methodology/approach The proposed VO incorporates the direct method with the indirect method to track the features and to optimize the camera pose. It initializes the positions of tracked pixels with the IMU information. Besides, the tracked pixels are refined by minimizing the photometric errors. Due to the small convergence radius of the indirect method, the dynamic pixels are rejected. Subsequently, the camera pose is optimized by minimizing the reprojection errors. The frames with little dynamic information are selected to create keyframes. Finally, the local bundle adjustment is performed to refine the poses of the keyframes and the positions of 3-D points. Findings The proposed VO approach is evaluated experimentally in dynamic environments with various motion types, suggesting that the proposed approach achieves more accurate and stable location than the conventional approach. Moreover, the proposed VO approach works well in the environments with the motion blur. Originality/value The proposed approach fuses the indirect method and the direct method with the IMU information, which improves the localization in dynamic environments significantly.

scholarly journals UAVS ENHANCED NAVIGATION IN OUTDOOR GNSS DENIED ENVIRONMENT USING UWB AND MONOCULAR CAMERA SYSTEMS

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 665-672 ◽  
Author(s):  
S. Zahran ◽  
A. Masiero ◽  
M. M. Mostafa ◽  
A. M. Moussa ◽  
A. Vettore ◽  
...  
Keyword(s):  
Navigation System ◽  
Low Cost ◽  
Global Navigation Satellite Systems ◽  
Ultra Wideband ◽  
Measurement Unit ◽  
Satellite Systems ◽  
Monocular Camera ◽  
Camera Systems ◽  
Global Navigation Satellite ◽  
The Cost

<p><strong>Abstract.</strong> The demand for small Unmanned Aerial Vehicles (UAVs) is massively increasing these days, due to the wide variety of applications utilizing such vehicles to perform tasks that may be dangerous or just to save time, effort, or cost. Small UAVs navigation system mainly depends on the integration between Global Navigation Satellite Systems (GNSS) and Inertial Measurement Unit (INS) to estimate the Positions, Velocities, and Attitudes (PVT) of the vehicle. Without GNSS such UAVs cannot navigate for long periods of time depending on INS alone, as the low-cost INS typically exhibits massive accumulation of errors during GNSS absence. Given the importance of ensuring full operability of the UAVs even during GNSS signals unavailability, other sensors must be used to bound the INS errors and enhance the navigation system performance. This paper proposes an enhanced UAV navigation system based on integration between monocular camera, Ultra-Wideband (UWB) system, and INS. In addition to using variable EKF weighting scheme. The paper also investigates this integration in the case of low density of UWB anchors, to reduce the cost required for such UWB system infrastructure. A GoPro Camera and UWB rover were attached to the belly of a quadcopter, an on the shelf commercial drone (3DR Solo), during the experimental flight. The velocity of the vehicle is estimated with Optical Flow (OF) from camera successive images, while the range measurements between the UWB rover and the stationary UWB anchors, which were distributed on the field, were used to estimate UAV position.</p>

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