A Novel KGP Algorithm for Improving INS/GPS Integrated Navigation Positioning Accuracy

A visual–inertial odometer is used to fuse the image information obtained by a vision sensor with the data measured by an inertial sensor and recover the motion track online in a global frame. However, in an indoor environment, geometric transformation, sparse features, illumination changes, blurring, and noise will occur, which will either cause a reduction in or failure of the positioning accuracy. To solve this problem, a map matching algorithm based on an indoor plane structure map is proposed to improve the positioning accuracy of the system; this algorithm was implemented using a conditional random field model. The output of the attitude information from the visual–inertial odometer was used as the input of the conditional random field model. The feature function between the attitude information and the expected value was established, and the maximum probabilistic value of the attitude was estimated. Finally, the closed-loop feedback correction of the visual–inertial system was carried out with the probabilistic attitude value. A number of experiments were designed to verify the feasibility and reliability of the positioning method proposed in this paper.

Section: Conditional Random Field Modelmentioning

confidence: 99%

Section: Best Path Matchingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improving Positioning Accuracy via Map Matching Algorithm for Visual–Inertial Odometer

Meng

Ren

Wang

et al. 2020

“…Some authors have created learning models that combine both LSTM and CNN approaches (e.g., [54]) while others have favored using ensemble learning methods in lieu of neural networks [55,56]. The majority of the models in the literature involve position or velocity estimation; however, these are not the only quantities that can be estimated.…”

Section: Related Workmentioning

confidence: 99%

Localization of Biobotic Insects Using Low-Cost Inertial Measurement Units

Cole

Bozkurt

Lobatón

2020

Disaster robotics is a growing field that is concerned with the design and development of robots for disaster response and disaster recovery. These robots assist first responders by performing tasks that are impractical or impossible for humans. Unfortunately, current disaster robots usually lack the maneuverability to efficiently traverse these areas, which often necessitate extreme navigational capabilities, such as centimeter-scale clearance. Recent work has shown that it is possible to control the locomotion of insects such as the Madagascar hissing cockroach (Gromphadorhina portentosa) through bioelectrical stimulation of their neuro-mechanical system. This provides access to a novel agent that can traverse areas that are inaccessible to traditional robots. In this paper, we present a data-driven inertial navigation system that is capable of localizing cockroaches in areas where GPS is not available. We pose the navigation problem as a two-point boundary-value problem where the goal is to reconstruct a cockroach’s trajectory between the starting and ending states, which are assumed to be known. We validated our technique using nine trials that were conducted in a circular arena using a biobotic agent equipped with a thorax-mounted, low-cost inertial measurement unit. Results show that we can achieve centimeter-level accuracy. This is accomplished by estimating the cockroach’s velocity—using regression models that have been trained to estimate the speed and heading from the inertial signals themselves—and solving an optimization problem so that the boundary-value constraints are satisfied.

“…The strap-down inertial navigation system (SINS) has been widely used in the determination of attitude, velocity and position of an object based on the dead-reckoning by making use of the measurements provided by the inertial measurement unit (IMU) [1][2][3]. To reduce the long-time navigation error mainly caused by the bias of accelerometers and gyroscopes, the SINS is often integrated with the global positioning system (GPS), which constructs the SINS/GPS integrated navigation system [4][5][6][7][8]. The heart of guaranteeing the performance of SINS is to accomplish the initial alignment and obtain an accurate initial condition [9,10].…”

Section: Introductionmentioning

confidence: 99%

A New In-Flight Alignment Method with an Application to the Low-Cost SINS/GPS Integrated Navigation System

Zhang

et al. 2020

The optimization-based alignment (OBA) methods, which are implemented by the optimal attitude estimation using vector observations—also called double-vectors—have proven to be effective at solving the in-flight alignment (IFA) problem. However, the traditional OBA methods are not applicable for the low-cost strap-down inertial navigation system (SINS) since the error of double-vectors will be accumulated over time due to the substantial drift of micro-electronic- mechanical system (MEMS) gyroscope. Moreover, the existing optimal estimation method is subject to a large computation burden, which results in a low alignment speed. To address these issues, in this article we propose a new fast IFA method based on modified double-vectors construction and the gradient descent method. To be specific, the modified construction method is implemented by reducing the integration interval and identifying the gyroscope bias during the construction procedure, which improves the accuracy of double-vectors and IFA; the gradient descent scheme is adopted to estimate the optimal attitude of alignment without complex matrix operation, which results in the improvement of alignment speed. The effect of different sizes of mini-batch on the performance of the gradient descent method is also discussed. Extensive simulations and vehicle experiments demonstrate that the proposed method has better accuracy and faster alignment speed than the related traditional methods for the low-cost SINS/global positioning system (GPS) integrated navigation system