An inertial navigation system for small autonomous underwater vehicles

A Small AUV Navigation System (SANS) is being developed at the Naval Postgraduate School. The SANS is an integrated GPS/inertial navigation system composed of low-cost, small-size components. It is designed to demonstrate the feasibility of using a low-cost inertial measurement unit to navigate between intermittent GPS fixes. This paper reports recent improvements to the SANS hardware, latest testing results and development of an asynchronous Kalman filter for improved position estimation.     

卡尔曼滤波器在滤波器组中的应用

考虑到信号经过分析滤波器组后,各子带信号会受到污染的情况,本文用多速率卡尔曼综合滤波器取代常规综合滤波器组,从仿真结果说明对输入信号重构性的改善是明显的.     

Kinematic modeling of wheeled mobile robots with slip

This work presents a kinematic modeling method for wheeled mobile robots with slip based on physical principles. First, we present the kinematic modeling of a mobile robot with no-slip considering four types of wheels: fixed, centered orientable, off-centered orientable (castor) and Swedish (also called Mecanum, Ilon or universal). Then, the dynamics of a wheeled mobile robot based on Lagrange formulation are derived and discussed. Next, a quasi-static motion is considered to obtain the kinematic conditions that provide the slip modeling equations. Several types of traction models for the slip between the wheel and the floor are indicated. In particular, for a frictional force linearly dependent on the sliding velocity, the no-slip kinematic equation of the wheeled mobile robot is related, through the weighted least-squares algorithm, with the slip modeling equations. To illustrate the applications of the proposed approach a tricycle vehicle is considered in a real situation. The experimental results obtained for the slip kinematic model are compared with the ones obtained for the well-known Kalman filter.     

Slip-compensated path following for planetary exploration rovers

A system that enables continuous slip compensation for a Mars rover has been designed, implemented and field-tested. This system is composed of several components that allow the rover to accurately and continuously follow a designated path, compensate for slippage and reach intended goals in high-slip environments. These components include visual odometry, vehicle kinematics, a Kalman filter pose estimator and a slip-compensated path follower. Visual odometry tracks distinctive scene features in stereo imagery to estimate rover motion between successively acquired stereo image pairs. The kinematics for a rocker–bogie suspension system estimates vehicle motion by measuring wheel rates, and rocker, bogie and steering angles. The Kalman filter processes measurements from an inertial measurement unit and visual odometry. The filter estimate is then compared to the kinematic estimate to determine whether slippage has occurred, taking into account estimate uncertainties. If slippage is detected, the slip vector is calculated by differencing the current Kalman filter estimate from the kinematic estimate. This slip vector is then used to determine the necessary wheel velocities and steering angles to compensate for slip and follow the desired path.     

Seam tracking based on estimation of weld position using Kalman filtering

Abstract

A seam tracking method is presented based on the estimation of weld position during the gas tungsten arc welding process. Kalman filtering of the weld pool images from a visual sensor is applied to compute recursively the solution to the weld position equations which are established based on an estimation of the centroid position of the weld pool images. This centroid, the position of which corresponds with the weld position, is extracted as the measurement eigenvector. The evolution of the weld position data from the weld pool images can be described through an appropriate process model, so that the weld position can be detected by applying a Kalman filter. This allows adjustment of the welding torch position in real time, which may significantly reduce processing time and promote seam tracking accuracy. Simulations and actual welding experiments have demonstrated the effectiveness of the proposed algorithm in the presence of weld pool image noise and have demonstrated the robustness of weld position detection for seam tracking.     

Differential GPS and odometry-based outdoor navigation of a mobile robot

This paper describes outdoor navigation for a mobile robot by using differential GPS (DGPS) and odometry in a campus walkway environment. The robot position is estimated by fusion of DGPS and odometry. The GPS receiver measures its position by radio waves from GPS satellites. The error of GPS measurement data increases near high buildings and trees because of multi-path and forward diffractions. Thus, it is necessary to pick up only accurate DGPS measurement data when the robot position is modified by fusing DGPS and odometry. In this paper, typical DGPS measurement data observed near high buildings and trees are reported. Then, the authors propose a novel position correction method by fusing GPS and odometry. Fusion of DGPS and odometry is realized using an extended Kalman filter framework. Moreover, outdoor navigation for a mobile robot is accomplished by using the proposed correction method.     

Wrist Camera-Based Vibration Suppression Control for a Flexible Manipulator

This paper addresses a vision-based method for estimating vibration excited in the tip of a flexible-link manipulator. In this method, estimation of vibration is achieved by observing the variation of image features projected on a wrist camera. It mimics the situation of utilizing a wrist camera in tip vibration control of a space manipulator. In space, a vision sensor can be expected to be a feasible means for measuring the elastic vibration of the space manipulators, since they are more reliable compared with sensors like strain gauges. The method proposed in this paper takes advantage of the frequential characteristics of visual information that are reflected as a blurred background scene. With the high-frequency component of the projected image features, a Kalman filter-based observer is implemented as the estimator for the vibration. This implementation is characterized by the considerations of incorporating the slow sensor of the camera in the fast servo loop and compensation of the time delay due to image processing. With the vibration estimator, vibration suppression control relying solely on a wrist camera becomes possible. This scheme is successfully verified by experiments.     

Efficient Simultaneous Localization and Mapping Based on Ceiling-View: Ceiling Boundary Feature Map Approach

For simultaneous localization and mapping (SLAM) based on the extended Kalman filter, the size of the state vector is an essential factor because the feasibility depends on it. In this paper, a new SLAM based on ceiling vision (cv-SLAM) is proposed. To keep the size of the state vector compact, the boundaries between ceiling and walls are used as landmarks for visual SLAM (vSLAM). The ceiling boundaries are robust to illuminative variations and they are not as numerous as the point features. Some constraints are imposed on the features based on the characteristics of the ceiling boundaries and an efficient update method called 'double update' is proposed to improve the SLAM performance. The basic idea of the double update is to fully utilize the intersections of the boundary features. Finally, the proposed SLAM is applied to some simulations and experiment, and its effectiveness is demonstrated through them.     

A Simultaneous Localization and Mapping Algorithm in Complex Environments: SLASEM

In this paper we present an algorithm for the application of simultaneous localization and mapping in complex environments. Instead of building a grid map or building a feature map with a small number of the obstacles' geometric parameters, the proposed algorithm builds a sampled environment map (SEM) to represent a complex environment with a set of environment samples. To overcome the lack of one-toone correspondence between environment samples and raw observations, the signed orthogonal distance function is proposed to be used as the observation model. A method considering geometric constraints is presented to remove redundant environment samples from the SEM. We also present a method to improve the SEM's topological consistency by using corner constraints. The proposed algorithm has been verified in a simulation and an indoor experiment. The results show that the algorithm can localize the robot and build a complex map effectively.