This paper presents a method for computing position and attitude of an instrument attached to the human body such as a
handheld or head-mounted video camera. The system uses two Inertial Measurement Units (IMUs). One IMU is part of
our earlier-developed Personal Dead-Reckoning (PDR) system, which tracks the position and heading of a walking person
relative to a known starting position. The other IMU is rigidly attached to the handheld or head-mounted instrument.
Our existing PDR system is substantially more accurate than conventional IMU-based systems because the IMU is
mounted on the foot of the user where error correction techniques can be applied that are unavailable for IMUs mounted
anywhere else on the body. However, if the walker is waving a handheld or head-mounted instrument, the position and
attitude of the instrument is not known. Equipping the instrument with an additional IMU is by itself an unsatisfactory
solution because that IMU is subject to accelerometer and gyro drift, which, unlike in the case of the foot-mounted IMU,
cannot be corrected and cause unbounded position and heading errors. Our approach uses transfer alignment techniques
and takes advantage of the fact that the handheld IMU moves with the walker. This constraint is used to bound and correct
errors by a Kalman filter. The paper explains our method and presents extensive experimental results. The results
show up to a five-fold reduction in heading errors for the handheld IMU.
This paper describes recent advances with our earlier developed Personal Dead-reckoning (PDR) system for GPS-denied
environments. The PDR system uses a foot-mounted Inertial Measurement Unit (IMU) that also houses a three axismagnetometer.
In earlier work we developed methods for correcting the drift errors in the accelerometers, thereby
allowing very accurate measurements of distance traveled. In addition, we developed a powerful heuristic method for
correcting heading errors caused by gyro drift. The heuristics exploit the rectilinear features found in almost all manmade
structures and therefore limit this technology to indoor use only.
Most recently we integrated a three-axis magnetometer with the IMU, using a Kalman Filter. While it is well known that
the ubiquitous magnetic disturbances found in most modern buildings render magnetometers almost completely useless
indoors, these sensors are nonetheless very effective in pristine outdoor environments as well as in some tunnels and
The present paper describes the integrated magnetometer/IMU system and presents detailed experimental results.
Specifically, the paper reports results of an objective test conducted by Firefighters of California's CAL-FIRE. In this
particular test, two firefighters in full operational gear and one civilian hiked up a two-mile long mountain trail over
rocky, sometimes steeply inclined terrain, each wearing one of our magnetometer-enhanced PDR systems but not using
any GPS. During the hour-long hike the average position error was about 20 meters and the maximum error was less
than 45 meters, which is about 1.4% of distance traveled for all three PDR systems.
In multi-agent scenarios, there can be a disparity in the quality of position estimation amongst the various agents. Here,
we consider the case of two agents - a leader and a follower - following the same path, in which the follower has a significantly
better estimate of position and heading. This may be applicable to many situations, such as a robotic "mule"
following a soldier. Another example is that of a convoy, in which only one vehicle (not necessarily the leading one) is
instrumented with precision navigation instruments while all other vehicles use lower-precision instruments. We present
an algorithm, called Follower-derived Heading Correction (FDHC), which substantially improves estimates of the
leader's heading and, subsequently, position. Specifically, FHDC produces a very accurate estimate of heading errors
caused by slow-changing errors (e.g., those caused by drift in gyros) of the leader's navigation system and corrects those
This paper pertains to the reduction of measurement errors due to drift in rate gyros used for tracking the position of
moving vehicles. In these applications, gyros and odometry are often used to augment GPS when GPS reception is unavailable.
Drift in gyros causes the unbounded growth of errors in the estimation of heading, rendering low-cost gyros
almost entirely useless in applications that require good accuracy for more than just a few seconds or minutes. Our proposed
method, called "Heuristic Drift Reduction" (HDR), applies a unique closed-loop control system approach to estimate
drift in real-time and remove the estimated drift instantaneously from the gyro reading. The paper presents results
of experiments, in which a gyro-equipped car was driven hundreds of miles on highways, rural roads, and city streets.
HDR reduced the average heading error over all of these drives by one order of magnitude.
The paper pertains to the reduction of measurement errors in gyroscopes used for tracking the position of walking
persons. Some of these tracking systems commonly use inertial or other means to measure distance traveled, 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.
This paper introduces a positioning system for walking persons, called "Personal Dead-reckoning" (PDR) system. The
PDR system does not require GPS, beacons, or landmarks. The system is therefore useful in GPS-denied environments,
such as inside buildings, tunnels, or dense forests. Potential users of the system are military and security personnel as
well as emergency responders.
The PDR system uses a small 6-DOF inertial measurement unit (IMU) attached to the user's boot. The IMU provides
rate-of-rotation and acceleration measurements that are used in real-time to estimate the location of the user relative
to a known starting point. In order to reduce the most significant errors of this IMU-based system−caused by the
bias drift of the accelerometers−we implemented a technique known as "Zero Velocity Update" (ZUPT). With the
ZUPT technique and related signal processing algorithms, typical errors of our system are about 2% of distance traveled.
This typical PDR system error is largely independent of the gait or speed of the user. When walking continuously for
several minutes, the error increases gradually beyond 2%. The PDR system works in both 2-dimensional (2-D) and 3-D
environments, although errors in Z-direction are usually larger than 2% of distance traveled.
Earlier versions of our system used an impractically large IMU. In the most recent version we implemented a much
smaller IMU. This paper discussed specific problems of this small IMU, our measures for eliminating these problems,
and our first experimental results with the small IMU under different conditions.
Most research on off-road mobile robot sensing focuses on obstacle negotiation, path planning, and position estimation. These issues have conventionally been the foremost factors limiting the performance and speeds of mobile robots. Very little attention has been paid to date to the issue of terrain trafficability, that is, the terrain's ability to support vehicular traffic. Yet, trafficability is of great importance if mobile robots are to reach speeds that human-driven vehicles can reach on rugged terrain. For example, it is obvious that the maximal allowable speed for a turn is lower when driving over sand or wet grass than when driving on packed dirt or asphalt. This paper presents our work on automated real-time characterization of terrain with regard to trafficability for small mobile robots. The two proposed methods can be implemented on skid-steer mobile robots and possibly also on tracked mobile robots. The paper also presents experimental results for each of the two implemented methods.
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, in recent years 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. A unique new mobile robot is the Segway Robotics Mobility Platform (RMP). This functionally close relative of the innovative Segway Human Transporter (HT) stabilizes a statically unstable single-axle robot dynamically, based on the principle of the inverted pendulum. While this approach works very well for human transportation, it introduces as unique set of challenges to navigation equipment using an onboard gyro. This is due to the fact that in operation the Segway RMP constantly changes its forward tilt, to prevent dynamically falling over. This paper introduces our new Fuzzy Logic Expert rule-based navigation (FLEXnav) method for fusing data from multiple gyroscopes and accelerometers in order to estimate accurately the attitude (i.e., heading and tilt) of a mobile robot. The attitude information is then further fused with wheel encoder data to estimate the three-dimensional position of the mobile robot. We have further extended this approach to include the special conditions of operation on the Segway RMP. The paper presents experimental results of a Segway RMP equipped with our system and running over moderately rugged terrain.
This paper presents an analysis of odometry errors in over-constrained mobile robots, that is, vehicles that have more independent motors than degrees of freedom.
Based on our analysis we developed and examined three novel error-reducing methods. One method, called “Fewest Pulses” method, makes use of the observation that most terrain irregularities, as well as wheel slip, result in an erroneous over-count of encoder pulses. A second method, called “Cross-coupled Control,” optimizes the motor control algorithm of the robot to reduce synchronization errors that would otherwise result in wheel slip with conventional controllers. The third method is based on so-called Expert Rules. With this method readings from redundant encoders are compared and utilized in different ways, according to predefined rules.
In the work described here we implemented our three error reducing methods on a modified Pioneer AT skid-steer platform and compared their odometric accuracy. The results in this paper point to clear advantages of the Expert Rule-based method over the other tested methods.