Invariant EKF Design for Scan Matching-aided Localization

Localization in indoor environments is a technique which estimates the robot's pose by fusing data from onboard motion sensors with readings of the environment, in our case obtained by scan matching point clouds captured by a low-cost Kinect depth camera. We develop both an Invariant Extended Kalman Filter (IEKF)-based and a Multiplicative Extended Kalman Filter (MEKF)-based solution to this problem. The two designs are successfully validated in experiments and demonstrate the advantage of the IEKF design

A Comprehensive Introduction of Visual-Inertial Navigation

In this article, a tutorial introduction to visual-inertial navigation(VIN) is presented. Visual and inertial perception are two complementary sensing modalities. Cameras and inertial measurement units (IMU) are the corresponding sensors for these two modalities. The low cost and light weight of camera-IMU sensor combinations make them ubiquitous in robotic navigation. Visual-inertial Navigation is a state estimation problem, that estimates the ego-motion and local environment of the sensor platform. This paper presents visual-inertial navigation in the classical state estimation framework, first illustrating the estimation problem in terms of state variables and system models, including related quantities representations (Parameterizations), IMU dynamic and camera measurement models, and corresponding general probabilistic graphical models (Factor Graph). Secondly, we investigate the existing model-based estimation methodologies, these involve filter-based and optimization-based frameworks and related on-manifold operations. We also discuss the calibration of some relevant parameters, also initialization of state of interest in optimization-based frameworks. Then the evaluation and improvement of VIN in terms of accuracy, efficiency, and robustness are discussed. Finally, we briefly mention the recent development of learning-based methods that may become alternatives to traditional model-based methods.Comment: 35 pages, 10 figure

Computationally-efficient visual inertial odometry for autonomous vehicle

This thesis presents the design, implementation, and validation of a novel nonlinearfiltering based Visual Inertial Odometry (VIO) framework for robotic navigation in GPSdenied environments. The system attempts to track the vehicle’s ego-motion at each time instant while capturing the benefits of both the camera information and the Inertial Measurement Unit (IMU). VIO demands considerable computational resources and processing time, and this makes the hardware implementation quite challenging for micro- and nanorobotic systems. In many cases, the VIO process selects a small subset of tracked features to reduce the computational cost. VIO estimation also suffers from the inevitable accumulation of error. This limitation makes the estimation gradually diverge and even fail to track the vehicle trajectory over long-term operation. Deploying optimization for the entire trajectory helps to minimize the accumulative errors, but increases the computational cost significantly. The VIO hardware implementation can utilize a more powerful processor and specialized hardware computing platforms, such as Field Programmable Gate Arrays, Graphics Processing Units and Application-Specific Integrated Circuits, to accelerate the execution. However, the computation still needs to perform identical computational steps with similar complexity. Processing data at a higher frequency increases energy consumption significantly. The development of advanced hardware systems is also expensive and time-consuming. Consequently, the approach of developing an efficient algorithm will be beneficial with or without hardware acceleration. The research described in this thesis proposes multiple solutions to accelerate the visual inertial odometry computation while maintaining a comparative estimation accuracy over long-term operation among state-ofthe- art algorithms. This research has resulted in three significant contributions. First, this research involved the design and validation of a novel nonlinear filtering sensor-fusion algorithm using trifocal tensor geometry and a cubature Kalman filter. The combination has handled the system nonlinearity effectively, while reducing the computational cost and system complexity significantly. Second, this research develops two solutions to address the error accumulation issue. For standalone self-localization projects, the first solution applies a local optimization procedure for the measurement update, which performs multiple corrections on a single measurement to optimize the latest filter state and covariance. For larger navigation projects, the second solution integrates VIO with additional pseudo-ranging measurements between the vehicle and multiple beacons in order to bound the accumulative errors. Third, this research develops a novel parallel-processing VIO algorithm to speed up the execution using a multi-core CPU. This allows the distribution of the filtering computation on each core to process and optimize each feature measurement update independently. The performance of the proposed visual inertial odometry framework is evaluated using publicly-available self-localization datasets, for comparison with some other open-source algorithms. The results illustrate that a proposed VIO framework is able to improve the VIO’s computational efficiency without the installation of specialized hardware computing platforms and advanced software libraries

On-Manifold Preintegration for Real-Time Visual-Inertial Odometry

Current approaches for visual-inertial odometry (VIO) are able to attain highly accurate state estimation via nonlinear optimization. However, real-time optimization quickly becomes infeasible as the trajectory grows over time, this problem is further emphasized by the fact that inertial measurements come at high rate, hence leading to fast growth of the number of variables in the optimization. In this paper, we address this issue by preintegrating inertial measurements between selected keyframes into single relative motion constraints. Our first contribution is a \emph{preintegration theory} that properly addresses the manifold structure of the rotation group. We formally discuss the generative measurement model as well as the nature of the rotation noise and derive the expression for the \emph{maximum a posteriori} state estimator. Our theoretical development enables the computation of all necessary Jacobians for the optimization and a-posteriori bias correction in analytic form. The second contribution is to show that the preintegrated IMU model can be seamlessly integrated into a visual-inertial pipeline under the unifying framework of factor graphs. This enables the application of incremental-smoothing algorithms and the use of a \emph{structureless} model for visual measurements, which avoids optimizing over the 3D points, further accelerating the computation. We perform an extensive evaluation of our monocular \VIO pipeline on real and simulated datasets. The results confirm that our modelling effort leads to accurate state estimation in real-time, outperforming state-of-the-art approaches.Comment: 20 pages, 24 figures, accepted for publication in IEEE Transactions on Robotics (TRO) 201

Joint Localization Based on Split Covariance Intersection on the Lie Group

This paper presents a pose fusion method that accounts for the possible correlations among measurements. The proposed method can handle data fusion problems whose uncertainty has both independent part and dependent part. Different from the existing methods, the uncertainties of the various states or measurements are modeled on the Lie algebra and projected to the manifold through the exponential map, which is more precise than that modeled in the vector space. The dealing of the correlation is based on the theory of covariance intersection, where the independent and dependent parts are split to yield a more consistent result. In this paper, we provide a novel method for correlated pose fusion algorithm on the manifold. Theoretical derivation and analysis are detailed first, and then the experimental results are presented to support the proposed theory. The main contributions are threefold: (1) We provide a theoretical foundation for the split covariance intersection filter performed on the manifold, where the uncertainty is associated on the Lie algebra. (2) The proposed method gives an explicit fusion formalism on SE(3) and SE(2), which covers the most use cases in the field of robotics. (3) We present a localization framework that can work both for single robot and multi-robots systems, where not only the fusion with possible correlation is derived on the manifold, the state evolution and relative pose computation are also performed on the manifold. Experimental results validate its advantage over state-of-the-art methods

Robust state estimation methods for robotics applications

State estimation is an integral component of any autonomous robotic system. Finding the correct position, velocity, and orientation of an agent in its environment enables it to do other tasks like mapping and interacting with the environment, and collaborating with other agents. State estimation is achieved by using data obtained from multiple sensors and fusing them in a probabilistic framework. These include inertial data from Inertial Measurement Unit (IMU), images from camera, range data from lidars, and positioning data from Global Navigation Satellite Systems (GNSS) receivers. The main challenge faced in sensor-based state estimation is the presence of noisy, erroneous, and even lack of informative data. Some common examples of such situations include wrong feature matching between images or point clouds, false loop-closures due to perceptual aliasing (different places that look similar can confuse the robot), presence of dynamic objects in the environment (odometry algorithms assume a static environment), multipath errors for GNSS (signals for satellites jumping off tall structures like buildings before reaching receivers) and more. This work studies existing and new ways of how standard estimation algorithms like the Kalman filter and factor graphs can be made robust to such adverse conditions without losing performance in ideal outlier-free conditions. The first part of this work demonstrates the importance of robust Kalman filters on wheel-inertial odometry for high-slip terrain. Next, inertial data is integrated into GNSS factor graphs to improve the accuracy and robustness of GNSS factor graphs. Lastly, a combined framework for improving the robustness of non-linear least squares and estimating the inlier noise threshold is proposed and tested with point cloud registration and lidar-inertial odometry algorithms followed by an algorithmic analysis of optimizing generalized robust cost functions with factor graphs for GNSS positioning problem