An orientation estimation strategy for low cost IMU using a nonlinear Luenberger observer

Abstract Accurate attitude estimation using low-cost sensors is an important capability to enable many robotic applications. In this paper, we present a method based on the concept of correntropy in Kalman filtering to estimate the 3D orientation of a rigid body using a low-cost inertial measurement unit (IMU). We then leverage the proposed attitude estimation framework to develop a LiDAR-Intertial Odometry (LIO) demonstrating improved localization accuracy with respect to traditional methods. This is of particular importance when the robot undergoes high-rate motions that typically exacerbate the issues associated with low-cost sensors. The proposed orientation estimation approach is first validated using the data coming from a low-cost IMU sensor. We further demonstrate the performance of the proposed LIO solution in a simulated robotic cave exploration scenario.

Download Full-text

Body-Segment Orientation Estimation in Rider-Bicycle Interactions With an Un-Calibrated Monocular Camera and Wearable Gyroscopes

Volume 2: Control, Monitoring, and Energy Harvesting of Vibratory Systems; Cooperative and Networked Control; Delay Systems; Dynamical Modeling and Diagnostics in Biomedical Systems; Estimation and Id of Energy Systems; Fault Detection; Flow and Thermal Systems; Haptics and Hand Motion; Human Assistive Systems and Wearable Robots; Instrumentation and Characterization in Bio-Systems; Intelligent Transportation Systems; Linear Systems and Robust Control; Marine Vehicles; Nonholonomic Systems ◽

10.1115/dscc2013-3839 ◽

2013 ◽

Cited By ~ 1

Author(s):

Xiang Lu ◽

Yizhai Zhang ◽

Kaiyan Yu ◽

Jingang Yi ◽

Jingtai Liu

Keyword(s):

Low Cost ◽

High Accuracy ◽

Human Anatomy ◽

Upper Limbs ◽

Body Segment ◽

Orientation Estimation ◽

Monocular Camera ◽

Estimation Scheme ◽

Inertial Measurements ◽

Segment Orientation

We present a real-time human body-segment (e.g., upper limbs) orientation estimation scheme in rider-bicycle interactions. The estimation scheme is built on the fusion of measurements of an un-calibrated monocular camera on the bicycle and a set of small wearable gyroscopes attached to rider’s upper limbs. The known optical features are conveniently collocated with the gyroscopes. The design of an extended Kalman filter (EKF) to fuse the vision/inertial measurements compensates for the drifting errors by directly integrating gyroscope measurements. The characteristic and constraints from human anatomy and the rider-bicycle interactions are used to enhance the EKF performance. We demonstrate the effectiveness of the estimation design through bicycle riding experiments. The attractive properties of the proposed pose estimation in human-machine interactions include low-cost, high-accuracy, and wearable configurations for outdoor personal activities. Although we only present the application for rider-bicycle interactions, the proposed estimation scheme is readily extended and used for other types of human-machine interactions.

Download Full-text