Lauro Ojeda

Non-GPS navigation for security personnel and first responders

This paper introduces a “Personal Dead-reckoning” (PDR) navigation system for walking persons. The system is useful for monitoring the position of emergency responders inside buildings, where GPS is unavailable. The PDR system uses a six-axes Inertial Measurement Unit attached to the user’s boot. The system’s strength lies in the use of a technique known as “Zero Velocity Update” (ZUPT) that virtually eliminates the ill-effects of drift in the accelerometers. It works very well with different gaits, as well as on stairs, slopes, and generally on 3-dimensional terrain. This paper explains the PDR and presents extensive experimental results, which illustrate the utility and practicality of the system.

Accurate mobile robot dead-reckoning with a precision-calibrated fiber-optic gyroscope

This paper describes two methods aimed at improving dead-reckoning accuracy with fiber-optic gyroscopes (FOGs) in mobile robots. The first method is a precision calibration procedure for FOGs, which effectively reduces the ill effects of nonlinearity of the scale-factor and temperature dependency. The second method is the implementation of an indirect feedback Kalman filter that fuses the sensor data from the FOG with the odometry system of the mobile robot. The paper also provides experimental results and compares the relative effectiveness of the two methods as implemented on a four-wheel drive/skidsteer Pioneer AT mobile robot.

Terrain Characterization and Classification with a Mobile Robot

This paper introduces novel methods for terrain classification and characterization with a mobile robot. In the context of this paper, terrain classification aims at associating terrains with one of a few predefined, commonly known categories, such as gravel, sand, or asphalt. Terrain characterization, on the other hand, aims at determining key parameters of the terrain that affect its ability to support vehicular traffic. Such properties are collectively called “trafficability.” The proposed terrain classification and characterization system comprises a skid-steer mobile robot, as well as some common and some uncommon but optional onboard sensors. Using these components, our system can characterize and classify terrain in real time and during the robots actual mission. The paper presents experimental results for both the terrain classification and characterization methods. The methods proposed in this paper can likely also be implemented on tracked robots, although we did not test this option in our work.

Current-Based Slippage Detection and Odometry Correction for Mobile Robots and Planetary Rovers

This paper introduces a novel method for wheel-slippage detection and correction based on motor current measurements. Our proposed method estimates wheel slippage from motor current measurements, and adjusts encoder readings affected by wheel slippage accordingly. The correction of wheel slippage based on motor currents works only in the direction of motion, but not laterally, and it requires some knowledge of the terrain. However, this knowledge does not have to be provided ahead of time by human operators. Rather, we propose three tuning techniques for determining relevant terrain parameters automatically, in real time, and during motion over unknown terrain. Two of the tuning techniques require position ground truth (i.e., GPS) to be available either continuously or sporadically. The third technique does not require any position ground truth, but is less accurate than the two other methods. A comprehensive set of experimental results have been included to validate this approach

Wheel slippage and sinkage detection for planetary rovers

Mobile robots are increasingly being used in high-risk rough terrain situations, such as planetary exploration and military applications. Current control and localization algorithms are not well suited to rough terrain, since they generally do not consider the physical characteristics of the vehicle and its environment. Little attention has been devoted to the study of the dynamic effects occurring at the wheel-terrain interface, such as slip and sinkage. These effects compromise odometry accuracy, traction performance, and may even result in entrapment and consequent mission failure. This paper describes methods for wheel slippage and sinkage detection aiming at improving vehicle mobility on soft sandy terrain. Novel measures for wheel slip detection are presented based on observing different onboard sensor modalities and defining deterministic conditions that indicate vehicle slippage. An innovative vision-based algorithm for wheel sinkage estimation is discussed based on edge detection strategy. Experimental results, obtained with a Mars rover-type robot operating in high-slippage sandy environments and with a wheel sinkage testbed, are presented to validate our approach. It is shown that these techniques are effective in detecting wheel slip and sinkage.

FLEXnav: fuzzy logic expert rule-based position estimation for mobile robots on rugged terrain

Most mobile robots use a combination of absolute and relative sensing techniques for position estimation. Relative positioning techniques are generally known as dead-reckoning. Many systems use odometry as their only dead-reckoning means. However, fiber optic gyroscopes have become more affordable and are being used on many platforms to supplement odometry, especially in indoor applications. Still, if the terrain is not level (i.e., rugged or rolling terrain), the tilt of the vehicle introduces errors into the conversion of gyro readings to vehicle heading. In order to overcome this problem vehicle tilt must be measured and factored into the heading computation. The paper introduces a new fuzzy logic expert rule-based navigation (FLEXnav) method for fusing data from multiple low- to medium-cost gyroscopes and accelerometers in order to estimate accurately the heading and tilt of a mobile robot. Experimental results of mobile robot runs over rugged terrain are presented, showing the effectiveness of our FLEXnav method.

Heuristic Reduction of Gyro Drift for Personnel Tracking Systems

The paper pertains to the reduction of measurement errors in gyroscopes used for tracking the position of walking persons. Such tracking systems commonly use inertial or other means to measure distance travelled, and one or more gyros to measure changes in heading. MEMS-type gyros or IMUs are best suited for this task because of their small size and low weight. However, these gyros have large drift rates and can be sensitive to accelerations. The Heuristic Drift Reduction (HDR) method presented in this paper estimates the drift component and eliminates it, reducing heading errors by almost one order of magnitude.

Heuristic Drift Elimination for Personnel Tracking Systems

This paper pertains to the reduction of the effects of measurement errors in rate gyros used for tracking, recording, or monitoring the position of persons walking indoors. In such applications, bias drift and other gyro errors can degrade accuracy within minutes. To overcome this problem we developed the Heuristic Drift Elimination (HDE) method, that effectively corrects bias drift and other slow-changing errors. HDE works by making assumptions about walking in structured, indoor environments. The paper explains the heuristic assumptions and the HDE method, and shows experimental results. In typical applications, HDE maintains near-zero heading errors in walks of unlimited duration. 1. I N T R O D U C T I O N. The primary choice for almost all outdoor land navigation tasks for vehicles or persons is GPS. In buildings, however, GPS is generally unavailable. One possible solution for indoor personnel tracking is based on Inertial Measurement Units (IMUs). IMUs comprise a 3-axes accelerometer and a 3-axes gyroscope. Both of these sensor modalities require, among other mathematical processing, that their signals be numerically integrated to produce the desired position and attitude information. The numeric integration has a tendency to cause errors due to drift. Drift is produced when small, slow-changing deviations from the correct signal are integrated with respect to time. The highly undesirable result of drift is that the error of the computed output – relative position or attitude – increases continuously and without bound. One can conceptually view drift as being composed of two components : a slowchanging component, called ‘‘ bias instability,’’ and a high-frequency noise component with an average of zero. The error contribution due to the integration of the highfrequency noise component is called ‘‘ Angle Random Walk ’’ (ARW). Generally, ARW creates only small errors in the computation of heading since its average is about zero. In the context of this paper, we are therefore concerned only with the slow-changing component of drift. Gyros are also sensitive to changes in temperature, and certain gyros are sensitive to linear accelerations. In our application, these two effects also produce errors that can be treated as having slow changing components, as does drift. Our proposed heuristic drift elimination method counteracts all slow-changing errors regardless of

Measurement of foot placement and its variability with inertial sensors

Gait parameters such as stride length, width, and period, as well as their respective variabilities, are widely used as indicators of mobility and walking function. Foot placement and its variability have thus been applied in areas such as aging, fall risk, spinal cord injury, diabetic neuropathy, and neurological conditions. But a drawback is that these measures are presently best obtained with specialized laboratory equipment such as motion capture systems and instrumented walkways, which may not be available in many clinics and certainly not during daily activities. One alternative is to fix inertial measurement units (IMUs) to the feet or body to gather motion data. However, few existing methods measure foot placement directly, due to drift associated with inertial data. We developed a method to measure stride-to-stride foot placement in unconstrained environments, and tested whether it can accurately quantify gait parameters over long walking distances. The method uses ground contact conditions to correct for drift, and state estimation algorithms to improve estimation of angular orientation. We tested the method with healthy adults walking over-ground, averaging 93 steps per trial, using a mobile motion capture system to provide reference data. We found IMU estimates of mean stride length and duration within 1% of motion capture, and standard deviations of length and width within 4% of motion capture. Step width cannot be directly estimated by IMUs, although lateral stride variability can. Inertial sensors measure walks over arbitrary distances, yielding estimates with good statistical confidence. Gait can thus be measured in a variety of environments, and even applied to long-term monitoring of everyday walking.

Precision calibration of fiber-optics gyroscopes for mobile robot navigation

Fiber-optics gyroscopes (gyros) are gaining importance as a means for improving dead-reckoning accuracy in mobile robots. In the past, the relatively high drift rate of moderately priced gyros presented the foremost technical limitation of these devices. More recently, fiber-optics gyros with very low drift rates have become available and affordable. Because of their low drift rate attention is warranted to sources of errors that were previously considered as of secondary importance. In the KVH E-Core RD2100 gyros that were examined at our lab we found that the nonlinearity of the scale-factor and temperature dependency caused significant errors. A calibration method, described in the paper, reduces the resulting errors by one order of magnitude.