Daniel M. Helmick

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.

Autonomy for Mars Rovers: Past, Present, and Future

The vehicles used to explore the Martian surface require a high degree of autonomy to navigate challenging and unknown terrain, investigate targets, and detect scientific events. Increased autonomy will be critical to the success of future missions. In July 1997, as part of NASAs Mars Pathfinder mission, the Sojourner rover became the first spacecraft to autonomously drive on another planet. The twin Mars Exploration Rovers (MER) vehicles landed in January 2004, and after four years Spirit had driven more than four miles and Opportunity more than seven miles-lasting well past their projected three-month lifetime and expected distances traveled. The newest member of the Mars rover family will have the ability to autonomously approach and inspect a target and automatically detect interesting scientific events. In fall 2009, NASA plans to launch the Mars Science Laboratory (MSL) rover, with a primary mission of two years of surface exploration and the ability to acquire and process rock samples. In the near future, the Mars Sample Return (MSR) mission, a cooperative project of NASA and the European Space Agency, will likely use a lightweight rover to drive out and collect samples and bring them back to an Earth return vehicle. This rover will use an unprecedented level of autonomy because of the limited lifetime of a return rocket on the Martian surface and the desire to obtain samples from distant crater walls.

Learning and prediction of slip from visual information

This paper presents an approach for slip prediction from a distance for wheeled ground robots using visual information as input. Large amounts of slippage which can occur on certain surfaces, such as sandy slopes, will negatively affect rover mobility. Therefore, obtaining information about slip before entering such terrain can be very useful for better planning and avoiding these areas. To address this problem, terrain appearance and geometry information about map cells are correlated to the slip measured by the rover while traversing each cell. This relationship is learned from previous experience, so slip can be predicted remotely from visual information only. The proposed method consists of terrain type recognition and nonlinear regression modeling. The method has been implemented and tested offline on several off-road terrains including: soil, sand, gravel, and woodchips. The final slip prediction error is about 20%. The system is intended for improved navigation on steep slopes and rough terrain for Mars rovers. (c) 2007 Wiley Periodicals, Inc.

Autonomous Stair Climbing for Tracked Vehicles

In this paper, an algorithm for autonomous stair climbing with a tracked vehicle is presented. The proposed method achieves robust performance under real-world conditions, without assuming prior knowledge of the stair geometry, the dynamics of the vehicles interaction with the stair surface, or lighting conditions. The approach relies on fast and accurate estimation of the robots heading and its position relative to the stair boundaries. An extended Kalman filter is used for quaternion-based attitude estimation, fusing rotational velocity measurements from a 3-axial gyroscope, and measurements of the stair edges acquired with an onboard camera. A two-tiered controller, comprised of a centering- and a heading-control module, utilizes the estimates to guide the robot rapidly, safely, and accurately upstairs. Both the theoretical analysis and implementation of the algorithm are presented in detail, and extensive experimental results demonstrating the algorithms performance are described.

Multi-sensor, high speed autonomous stair climbing

Small, tracked mobile robots designed for general urban mobility have been developed for the purpose of reconnaissance and/or search and rescue missions in buildings and cities. Autonomous stair climbing is a significant capability required for many of these missions. In this paper we present the design and implementation of a new set of estimation and control algorithms that increase the speed and effectiveness of stair climbing. We have developed: (1) a Kalman filter that fuses visual/laser data with inertial measurements and provides attitude estimates of improved accuracy at a high rate; and (2) a physics based controller that minimizes the heading error and maximizes the effective velocity of the vehicle during stair climbing. Experimental results using a tracked vehicle validate the improved performance of this control and estimation scheme over previous approaches.

Learning to predict slip for ground robots

In this paper we predict the amount of slip an exploration rover would experience using stereo imagery by learning from previous examples of traversing similar terrain. To do that, the information of terrain appearance and geometry regarding some location is correlated to the slip measured by the rover while this location is being traversed. This relationship is learned from previous experience, so slip can be predicted later at a distance from visual information only. The advantages of the approach are: 1) learning from examples allows the system to adapt to unknown terrains rather than using fixed heuristics or predefined rules; 2) the feedback about the observed slip is received from the vehicles own sensors which can fully automate the process; 3) learning slip from previous experience can replace complex mechanical modeling of vehicle or terrain, which is time consuming and not necessarily feasible. Predicting slip is motivated by the need to assess the risk of getting trapped before entering a particular terrain. For example, a planning algorithm can utilize slip information by taking into consideration that a slippery terrain is costly or hazardous to traverse. A generic nonlinear regression framework is proposed in which the terrain type is determined from appearance and then a nonlinear model of slip is learned for a particular terrain type. In this paper we focus only on the latter problem and provide slip learning and prediction results for terrain types, such as soil, sand, gravel, and asphalt. The slip prediction error achieved is about 15% which is comparable to the measurement errors for slip itself

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.

Algorithms and sensors for small robot path following

Tracked mobile robots in the 20 kg size class are under development for applications in urban reconnaissance. For efficient deployment, it is desirable for teams of robots to be able to automatically execute path following behaviors, with one or more followers tracking the path taken by a leader. The key challenges to enabling such a capability are (1) to develop sensor packages for such small robots that can accurately determine the path of the leader and (2) to develop path following algorithms for the subsequent robots. To date, we have integrated gyros, accelerometers, compass/inclinometers, odometry, and differential GPS into an effective sensing package. The paper describes the sensor package, sensor processing algorithm and path tracking algorithm we have developed for the leader/follower problem in small robots and shows the results of performance characterization of the system. We also document pragmatic lessons learned about design, construction, and electromagnetic interference issues particular to the performance of state sensors on small robots.

Fast Terrain Classification Using Variable-Length Representation for Autonomous Navigation

We propose a method for learning using a set of feature representations which retrieve different amounts of information at different costs. The goal is to create a more efficient terrain classification algorithm which can be used in real-time, onboard an autonomous vehicle. Instead of building a monolithic classifier with uniformly complex representation for each class, the main idea here is to actively consider the labels or misclassification cost while constructing the classifier. For example, some terrain classes might be easily separable from the rest, so very simple representation will be sufficient to learn and detect these classes. This is taken advantage of during learning, so the algorithm automatically builds a variable-length visual representation which varies according to the complexity of the classification task. This enables fast recognition of different terrain types during testing. We also show how to select a set of feature representations so that the desired terrain classification task is accomplished with high accuracy and is at the same time efficient. The proposed approach achieves a good trade-off between recognition performance and speedup on data collected by an autonomous robot.

Slip Prediction Using Visual Information.

This paper considers prediction of slip from a distance for wheeled ground robots using visual information as input. Large amounts of slippage which can occur on certain surfaces, such as sandy slopes, will negatively affect rover mobility. Therefore, obtaining information about slip before entering a particular terrain can be very useful for better planning and avoiding terrains with large slip. The proposed method is based on learning from experience and consists of terrain type recognition and nonlinear regression modeling. After learning, slip prediction is done remotely using only the visual information as input. The method has been implemented and tested offline on several off-road terrains including: soil, sand, gravel, and woodchips. The slip prediction error is about 20% of the step size.