Adaptive filtering for vision-aided inertial navigation

State estimation;Visual inertial odometry;Vision-aided inertial navigation;Time delays;Outliers

Lee, Kyuman ; Johnson, Eric N. Feron, Eric Aerospace Engineering ; Johnson, Eric N.

University:Georgia Institute of Technology

Department:Aerospace Engineering

关键词: State estimation;    Visual inertial odometry;    Vision-aided inertial navigation;    Time delays;    Outliers;   

Others  :  https://smartech.gatech.edu/bitstream/1853/60806/1/LEE-DISSERTATION-2018.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

A With the advent of unmanned aerial vehicles (UAVs), a major area of interest in the research field of UAVs has been vision-aided inertial navigation systems (V-INS). Many missions of UAVs often demand V-INS in more operational environments such as indoors, hostilities, and disasters. In V-INS, inertial measurement unit (IMU) dead reckoning generates the dynamic models of UAVs, and vision sensors extract information about the surrounding environment and determine features or points of interest. With these sensors, the most widelyused algorithm for estimating vehicle and feature states of V-INS is an extended Kalman filter (EKF). The design of the standard EKF does not inherently allow for time offsets between the timestamps of the IMU and vision data, and the necessary assumption of the EKF is Gaussian and white noise. In fact, sensor-related delays and measurement outliers that arise in various realistic conditions are unknown parameters. A lack of compensation of unknown parameters leads to a serious impact on the accuracy of the navigation systems. To compensate for uncertainties of the parameters, we require modified versions of the estimator or theincorporation of other techniques into the filter. The main purpose of this thesis is to develop reliable and robust V-INS for UAVs, in particular, those for situations pertaining to such unknown parameters. First, to fuse measurements with unknown time delays, this study incorporates parameter estimation into feature initialization and state estimation. Utilizing estimated delays and cross covariance, latency-adaptive filtering corrects residual, Jacobian, and covariance. In addition, feature correspondence in image processing front end rejects vision outliers, and then a chi-squared statistic test in filtering back end detects the remaining outliers of the vision data. For frequent outliers, noise-adaptive filtering using variational approximation for Bayesian inference computes the optimal noise precision matrices of the measurement outliers. Unfortunately, few researchers have treated outlier adaptation in V-INS in great detail. Results from flight dataset tests validate the improved accuracy of V-INS employing these adaptive filtering frameworks.