Technical session talks from ICRA 2012
TechTalks from event: Technical session talks from ICRA 2012
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Invariant Momentum-Tracking Kalman Filter for Attitude EstimationThis paper presents the development, simulation and experimental testing of a non-linear Kalman filter for attitude estimation. This non-linear filter is able to conserve the invariants of the Kalman filter, i.e., the expectations on state estimates and their covariances, by operating in the Lie algebra of SO(3) and along the trajectory of evolving angular momentum. The main feature of this novel discrete-time filter is that the linearization of the Gaussian uncertainty around these permanent trajectories leads to a locally optimal Kalman gain matrix. Results confirm that this Invariant Momentum-tracking Kalman Filter (IMKF) out-performs state-of-the-art approaches such as the Extended Kalman Filter (EKF), and Invariant Extended Kalman Filter (IEKF). At very-low sampling rates, EKFs suffer from divergence as the uncertainty propagation is corrupted by the underlying system approximations. The IMKF suffers no such problems according to the theoretical developments and results reported here.
Complementary Filtering Approach to Orientation Estimation Using Inertial Sensors OnlyPrecise and reliable estimation of orientation plays crucial role for any mobile robot operating in unknown environment. The most common solution to determination of the three orientation angles: pitch, roll, and yaw, relies on the Attitude and Heading Reference System (AHRS) that exploits inertial data fusion (accelerations and angular rates) with magnetic measurements. However, in real world applications strong vibration and disturbances in magnetic field usually cause this approach to provide poor results. Therefore, we have devised a new approach to orientation estimation using inertial sensors only. It is based on modified complementary filtering and was proved by precise laboratory testing using rotational tilt platform as well as by robot field-testing. In the final, the algorithm well outperformed the commercial AHRS solution based on magnetometer aiding.
Design of Complementary Filter for High-Fidelity Attitude Estimation Based on Sensor Dynamics Compensation with Decoupled PropertiesA high-fidelity attitude estimation technique for wide and irregular movements is proposed, in which heterogeneous inertial sensors are combined in complementary way. Although the working frequency ranges of each sensor are not necessarily complementary, inverse sensor models are utilized in order to restore the original movements. In the case of 3D rotation, the sensor dynamics displays a highly nonlinear property. Even if it is approximated by a linear system, the inverse model of a sensor tends to be non-proper and unstable. An idea is to decouple it into the dynamics compensation part approximated by a linear transfer function and the strictly nonlinear coordinate transformation part. Bandpass filters inserted before the coordinate transformation guarantee that the total transfer function becomes proper and stable. Particularly, the differential operator of a high-pass filter cancels the integral operator included in the dynamics compensation of the rate gyroscope, which causes instability. The proposed method is more beneficial than Kalman filter in terms of the implementation since it facilitates a systematic design of the filter.
A Low-Cost and Fail-Safe Inertial Navigation System for AirplanesA typical Inertial Navigation System (INS) fuses acceleration and angular rate readings with aiding measurements obtained by GPS and a compass. Here we present a robust state estimation framework based on the Extended Kalman Filter (EKF) applied to low-cost electronics typically installed on-board small unmanned airplanes. It uses airspeed measurements as a backup operation mode replacing GPS updates when temporarily unavailable. We demonstrate the applicability of the proposed approach to real-world scenarios using a challenging dataset recorded on-board a manned glider including long-term circling. A comparison between the normal operation mode and the backup solution reveals minimal difference between the respective orientation estimates, a position error growth sub-linear with time during GPS outage and a seamless transition back to GPS-based operation.
Robust Multi-Sensor, Day/Night 6-DOF Pose Estimation for a Dynamic Legged Vehicle in GPS-Denied EnvironmentsWe present a real-time system that enables a highly capable dynamic quadruped robot to maintain an accurate 6-DOF pose estimate (better than 0.5m over every 50m traveled) over long distances traversed through complex, dynamic outdoor terrain, during day and night, in the presence of camera occlusion and saturation, and occasional large external disturbances, such as slips or falls. The system fuses a stereo-camera sensor, inertial measurement units (IMU), and leg odometry with an Extended Kalman Filter (EKF) to ensure robust, low-latency performance. Extensive experimental results obtained from multiple field tests are presented to illustrate the performance and robustness of the system over hours of continuous runs over hundreds of meters of distance traveled in a wide variety of terrains and conditions.
Global Pose Estimation with Limited GPS and Long Range Visual OdometryHere we present an approach to estimate the global pose of a vehicle in the face of two distinct problems; first, when using stereo visual odometry for relative motion estimation, a lack of features at close range causes a bias in the motion estimate. The other challenge is localizing in the global coordinate frame using very infrequent GPS measurements. Solving these problems we demonstrate a method to estimate and correct for the bias in visual odometry and a sensor fusion algorithm capable of exploiting sparse global measurements. Our graph-based state estimation framework is capable of inferring global orientation using a unified representation of local and global measurements and recovers from inaccurate initial estimates of the state, as intermittently available GPS information may delay the observability of the entire state. We also demonstrate a reduction of the complexity of the problem to achieve real-time throughput. In our experiments, we show in an outdoor dataset with distant features where our bias corrected visual odometry solution makes a five-fold improvement in the accuracy of the estimated translation compared to a standard approach. For a traverse of 2km we demonstrate the capabilities of our graph-based state estimation approach to successfully infer global orientation with as few as 6 GPS measurements and with two-fold improvement in mean position error using the corrected visual odometry.