Evaluation of the EKF-Based Estimation Architectures for Data Fusion in Mobile Robots

Abstract

This paper presents evaluation of four different state estimation architectures exploiting the extended Kalman filter (EKF) for 6-DOF dead reckoning of a mobile robot. The EKF is a well proven and commonly used technique for fusion of inertial data and robot's odometry. However, different approaches to designing the architecture of the state estimator lead to different performance and computational demands. While seeking the best possible solution for the mobile robot, the nonlinear model and the error model are addressed, both with and without a complementary filter for attitude estimation. The performance is determined experimentally by means of precision of both indoor and outdoor navigation, including complex-structured environment such as stairs and rough terrain. According to the evaluation, the nonlinear model combined with the complementary filter is selected as a best candidate (reaching 0.8 m RMSE and average of 4% return position error (RPE) of distance driven) and implemented for real-time onboard processing during a rescue mission deployment. Index Terms-Complementary filter (CF), extended Kalman filter (EKF), urban search and rescue (USAR).

Simultaneous exploration and segmentation for search and rescue

“…The panoramic human body data set 7 was captured indoors using the mobile SAR platform depicted in Figure 1. During data capture, the robot localized itself using the ICP-based SLAM method from Pomerleau, Colas, Siegwart, and Magnenat (2013) and Simanek, Reinstein, and Kubelka (2015), fusing IMU measurements and odometry during dead reckoning. We recorded 23 sequences in total (see Table 2 for a summary) with the robot following a mostly straight path 8 during which it was stopping regularly to capture data, including the thermal images for 13 discretized camera views.…”

Dual closed‐loop tracking control for wheeled mobile robots via active disturbance rejection control and model predictive control

“…In recent decades, research works on a wheeled mobile robot (WMR) have been widely investigated owing to its broad applications in many fields. [1][2][3] The main theoretical and practical challenges for trajectory tracking control of the WMR are model uncertainties, disturbances, and system constraints. So far, many techniques have been developed for tracking control of the WMR.…”

Heading Estimation of Robot Combine Harvesters during Turning Maneuveres

“…This information needs to be fused in a way that reduces sensor uncertainties and the additional task of interpretation must be performed [6]. There are different approaches used for sensor fusion to obtain the position and direction of vehicles, such as complementary filters [7] or Kalman filters with various architectures [8,9,10,11,12,13], particle filters or sequential Monte Carlo methods [14,15,16,17,18]. Since the dynamic motion of a vehicle is non-linear, a non-linear dynamic model and extended Kalman filter are commonly used for navigation purposes.…”