Daniel S. Clouse

Path following using visual odometry for a Mars rover in high-slip environments

A system for autonomous operation of Mars rovers in high slip environments has been designed, implemented, and tested. This system is composed of several key technologies that enable the rover to accurately follow a designated path, compensate for slippage, and reach intended goals independent of the terrain over which it is traversing (within the mechanical constraints of the mobility system). These technologies include: visual odometry, full vehicle kinematics, a Kalman filter pose estimator, and a slip compensation/path follower. Visual odometry tracks distinctive scene features in stereo imagery to estimate rover motion between successively acquired stereo image pairs using a maximum likelihood motion estimation algorithm. The full vehicle kinematics for a rocker-bogie suspension system estimates motion, with a no-slip assumption, by measuring wheel rates, and rocker, bogie, and steering angles. The Kalman filter merges data from an inertial measurement unit (IMU) and visual odometry. This merged estimate is then compared to the kinematic estimate to determine (taking into account estimate uncertainties) if and how much slippage has occurred. If no statistically significant slippage has occurred then the kinematic estimate is used to complement the Kalman filter estimate. If slippage has occurred then a slip vector is calculated by differencing the current Kalman filter estimate from the kinematic estimate. This slip vector is then used, in conjunction with the inverse kinematics, to determine the necessary wheel velocities and steering angles to compensate for slip and follow the desired path.

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.

Small field-of-view star identification using Bayesian decision theory

We describe a simple autonomous star identification algorithm which is effective using a narrow field of view (FOV) (2 deg), making the use of a science camera for star identification feasible. This work extends that of Padgett and Kreutz-Delgado (1997) by setting decision thresholds using Bayesian decision theory. Our simulations show that when positional accuracy of imaged stars is 0.5 pixel (standard deviation) and the apparent brightness deviates by 0.8 unit stellar magnitude, the algorithm correctly identifies 96.0% of the sensor orientations, with less than a 0.3% rate of false positives.

Slip compensation for a Mars rover

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 compensation/path follower. Visual odometry tracks distinctive scene features in stereo imagery to estimate rover motion between successively acquired stereo image pairs. The vehicle kinematics for a rocker-bogie suspension system estimates motion by measuring wheel rates, and rocker, bogie, and steering angles. The Kalman filter merges data from an inertial measurement unit (IMU) and visual odometry. This merged estimate is then compared to the kinematic estimate to determine how much slippage has occurred, taking into account estimate uncertainties. If slippage has occurred then a 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.

Autonomy architecture for aerobot exploration of Saturnian moon Titan

The Huygens probe arrived at Saturns moon, Titan, January 14,2005, unveiling a world that is radically different from any other in the solar system. The data obtained, complemented by continuing observations from the Cassini spacecraft, show methane lakes, river channels and drainage basins, sand dunes, cryovolcanos and sierras. This has led to an enormous scientific interest in a follow-up mission to Titan, using a robotic lighter-than-air vehicle (or aerobot). Aerobots have modest power requirements, can fly missions with extended durations, and have very long distance traverse capabilities. They can execute regional surveys, transport and deploy scientific instruments and in-situ laboratory facilities over vast distances, and also provide surface sampling at strategic science sites. This describes our progress in the development of the autonomy technologies that will be required for exploration of Titan. We provide an overview of the autonomy architecture and some of its key components. We also show results obtained from autonomous flight tests conducted in the Mojave Desert.

An Autonomy Architecture for Aerobot Exploration of the Saturnian Moon Titan

The Huygens probe arrived at Saturns moon Titan on January 14, 2005, unveiling a world that is radically different from any other in the Solar system. The data obtained, complemented by continuing observations from the Cassini spacecraft, show methane lakes, river channels and drainage basins, sand dunes, cryovolcanos and sierras. This has lead to an enormous scientific interest in a follow-up mission to Titan, using a robotic lighter-than-air vehicle (or aerobot). Aerobots have modest power requirements, can fly missions with extended durations, and have very long distance traverse capabilities. They can execute regional surveys, transport and deploy scientific instruments and in-situ laboratory facilities over vast distances, and also provide surface sampling at strategic science sites. This paper describes our progress in the development of the autonomy technologies that will be required for exploration of Titan. We provide an overview of the autonomy architecture and some of its key components. We also show results obtained from autonomous flight tests conducted in the Mojave desert.

Performance Evaluation of Handoff for Instrument Placement

[Abstract] Single Cycle Instrument Placement (SCIP), the ability to autonomously approach rocks and place contact instruments within 1cm of selected features, has been identified as a high priority for increasing the efficiency and science return for planetary rover surface operations. Because of imprecise localization, it is necessary for a rover to visually keep track of targets, using images from onboard stereo cameras, as the rover navigates to them. “Handoff” is the problem of matching a tracked point in one camera to the corresponding point in another camera pair. Handoff facilitates visual tracking of a point using multiple camera pairs, allowing at any time the choice of camera pair with the best view of the portion of the scene that is of interest. A significant fraction of the tracking error budget is due to handoff. In this paper, we review several methods for solving what we refer to as the handoff problem and evaluate their performance in the context of integrated planetary rover test beds analogous to the MER Spirit and Opportunity vehicles.

Estimating the position of a sphere from range images

Describes two algorithms for accurately locating a spherical object in space using data collected from a scanning laser range finder. This work is designed to support the capture phase of a future rendezvous and sample return mission to Mars. The first algorithm finds the parameters of the sphere which optimally fit the set of data points. This method is shown to be quite slow, and unlikely to meet the accuracy requirements of the mission. The second algorithm makes a set of almost-independent estimates of the position of the center of the sphere, one from each scan line of data. These estimates are combined using regression to produce an aggregate estimate of the sphere position. This second algorithm is both fast enough, and accurate enough to meet mission requirements.

Covering a sphere with retroreflectors

One of the future missions for Mars involves returning a soil sample from the Martian surface to Earth. The sample will be deposited in a spherical canister, shot into Mars orbit and then subsequently captured by a spacecraft for the return journey. This paper discusses how retroreflectors can be placed on the orbiting sample canister with the objective of maximizing returned light from a scanning laser system. The retroreflectors are vital for acquisition of the sample canister during the terminal rendezvous phase (<5 km) of the capture. The identification of a retroreflector configuration relies extensively on Monte Carlo simulations. Computer simulations show that a spherical t-design yields a strong return for a 50 retroreflectors constellation. The return is calculated utilizing formulas for Rayleigh-Sommerfeld diffraction, and integrating over the surfaces of the retroreflector apertures for the specific orientation of the spherical container. At a distance of 5 km, in simulation the chosen configuration produces a return signal that is at least 5% of the return of a single retroreflector head-on approximately 99.99% of the time. On average, the return signal is 1.36 times the signal of a single retroreflector head-on. The results of the model and empirical results collected at a shorter distance are consistent.

Accurate and Robust Scene Reconstruction in the Presence of Misassociated Features for Aerial Sensing

AbstractGeoreferencing through aerial imagery has several applications, including remote sensing, real-time situational mission awareness, environmental monitoring, rescue and relief, map generatio...