Inertial navigation systems for mobile robots

Abstract

A low-cost solid-state inertial navigation system (INS) for mobile robotics applications is described. Error models for the inertial sensors are generated and included in an Extended Kalman Filter (EKF) for estimating the position and orientation of a moving robot vehicle. Two Merent solid-state gyroscopes have been evaluated for estimating the orientation of the robot. Performance of the gyroscopes with error models is compared to the performance when the error models are excluded from the system. The results demonstrate that without error compensation, the error in orientation is between 5-15"/min but can be improved at least by a factor of 5 if an adequate error model is supplied. S i a r error models have been developed for each axis of a solid-state triaxial accelerometer and for a conducting-bubble tilt sensor which may also be used as a low-cost accelerometer. Linear position estimation with information from accelerometers and tilt sensors is more susceptible to errors due to the double integration process involved in estimating position. With the system described here, the position drift rate is 1-8 cds, depending on the frequency of acceleration changes. An integrated inertial platform consisting of three gyroscopes, a triaxial accelerometer and two tilt sensors is described. Results from tests of this platform on a large outdoor mobile robot system are described and compared to the results obtained from the robot's own radar-based guidance system. Like all inertial systems, the platform requires additional information from some absolute position-sensing mechanism to overcome long-term drift. However, the results show that with careful and detailed modeling of error sources, low-cost inertial sensing systems can provide valuable orientation and position information particularly for outdoor mobile robot applications.

Open Solution for Humanoid Attitude Estimation through Sensory Integration and Extended Kalman Filtering

“…Many researchers face this problem using Extended Kalman Filters [15], [16], [17]. For instance, Rehbinder and Hu have previously designed an algorithm [18] for fusing inclinometer and gyro data assuming low translational accelerations, which may not be very realistic for a walking robot.…”

Heads Up and Camera Down: A Vision-Based Tracking Modality for Mobile Mixed Reality

“…A common approach for tracking pedestrians in the outdoors is to couple GPS tracking with inertial-based dead reckoning to improve update rates and to bridge areas where GPS is unreliable [20]. Relying on inertial sensors as a direct position tracking modality is widely deemed to be of limited use, however, because of the rapid propagation of drift errors due to double integration [4]. Instead, many systems employ inertial sensors as a pedometer, detecting the event of the user taking a step [5], [20], [21].…”

Global optimal attitude estimation using uncertainty ellipsoids

“…This center is obtained from the discrete equations of motion, (8), (9), and (10) applied to (Ĉ k ,ω k ):…”

“…A sample of the literature in spacecraft attitude estimation can be found in [4][5][6][7][8]. Applications of attitude estimation to unmanned vehicles and robots can be found in [2,[9][10][11]. Most existing attitude estimation schemes use generalized coordinate representations of the attitude.…”