Extending functionality of RF Ultrasound positioning system with dead-reckoning to accurately determine mobile robot's orientation

Abstract

In this paper we show how to accurately track position and orientation of a mobile robot using positioning only RF Ultrasound transceiver system and dead-reckoning. The solution is based on geometrical displacement of the positioning device from the axis of rotation of the robot which brings dependency in position of the device and robot's orientation and enables the correction of the absolute orientation of the robot. Estimation is done via Extended Kalman Filter with variable discretization time because of stochastic nature of measurement arrival times. We describe and analyze the main sources of noise in odometry and position device readings. We also suggest the method for measurement outlier elimination derived from empirical measurement analysis. Experimental verification of the proposed method is presented that yields position error aprox. 1±2.5 centimeters and 1±2 degrees.

Drone SLAM using TDOA-RSS signal with applied EKF on PF data

“…With a simulation we isolate variables which can influence control parameters in real -time drone environment. The test case scenario was to use a testbed, where a listener on the drone receives signals from beacons based on TDOA-RSS (Time Differential of Arrival -Receiver Signal Strength) method [2,5]. In this way, we have calculated the distance between each node and a listener.…”

“…Linearization in some cases can cause a problem because we don't stay in the Gaussian world if motion and/or measurement models are nonlinear functions of the state. Generally, there is no closed-form solution for Bayes filter [5]. The aim is to keep the functions and to approximate distributions.…”

“…General idea is to use six ultrasonic beacons with listener. This research and development platform (Crossbow Cricket) is modified for earlier scenario described in paper [2][3][4][5][6][7][8][9][10][11]. Simulations in Matlab are extended from 2D equations to 3D equation system.…”

Quadrocopter Hovering Using Position-estimation Information from Inertial Sensors and a High-delay Video System

“…Outdoors, the GPS (Global Positioning System) that relies on receiving its signal from satellites can be used, however, indoors, the quadrocopter must rely on its own sensors to determine its position in the environment or on a custom local indoor tracking system, that must be built. Indoor tracking systems are usually based either on beacon-based systems that use RF (Radio Frequency) waves (or a combination of RF and Ultrasonic waves [28]) or on an optical motion tracker (a constellation of video cameras with corresponding markers fitted to the quadrocopter) as in [3,20,25,27,34,35,37]. However, relying only on its own sensors and features in the environment makes a quadrocopter autonomous.…”

State Estimation of Multiagent Systems Under Impulsive Noise and Disturbances

“…Measurements from global positioning system (GPS) are often used by multi-agent system to determine the position and orientation of individual agents, but when some agents lack GPS information (either permanently or temporarily), one can still obtain information about the agents' positions using alternative sensor modalities, which include vision based-sensors [4,5], RF sensors [6,7], and acoustic sensors [8,9]. It turns out that these sensor modalities typically exhibit impulsive noise: vision-based sensors can exhibit large errors when a visual landmark is temporarily obstructed or mis-interpreted, and RF/acoustic sensors are prone to reporting false measurements due to multi-path reflections.…”