Cartographic databases can be kept up to date through aerial image analysis. Such analysis is optimized when one knows what parts of an aerial image are roads and when one knows locations of complex road structures, such as overpasses and intersections. This paper proposes self-supervised computer vision algorithms that analyze a publicly available cartographic resource (i.e., screenshots of road vectors) to, without human intervention, identify road image-regions and detects overpasses.

This paper presents new aerial image analysis algorithms that, from highway ortho-images, produce lane-level detailed maps. We analyze screenshots of road vectors to obtain the relevant spatial and photometric cues of road image-regions. We then refine the obtained patterns to generate hypotheses about the true road-lanes. A road-lane hypothesis, since it explains only a part of the true road-lane, is then linked to other hypotheses to completely delineate boundaries of the true road-lanes. Finally, some of the refined image cues about the underlying road network are used to guide a linking process of road-lane hypotheses.

The outdoor perception problem is a major challenge for driver-assistance and autonomous vehicle systems. While these systems can often employ active sensors such as sonar, radar, and lidar to perceive their surroundings, the state of standard traffic lights can only be perceived visually. By using a prior map, a perception system can anticipate and predict the locations of traffic lights and improve detection of the light state. The prior map also encodes the control semantics of the individual lights. This paper presents methods for automatically mapping the three dimensional positions of traffic lights and robustly detecting traffic light state onboard cars with cameras. We have used these methods to map more than four thousand traffic lights, and to perform onboard traffic light detection for thousands of drives through intersections.

We present a motion planner for autonomous highway driving that adapts the state lattice framework pioneered for planetary rover navigation to the structured environment of public roadways. The main contribution of this paper is a search space representation that allows the search algorithm to systematically and efficiently explore both spatial and temporal dimensions in real time. This allows the low-level trajectory planner to assume greater responsibility in planning to follow a leading vehicle, perform lane changes, and merge between other vehicles. We show that our algorithm can readily be accelerated on a GPU, and demonstrate it on an autonomous passenger vehicle.

An autonomous robotic vehicle can drive through and park in a lot more reliably if it is guided by a parking lot map. This specialized map creates structure, including the centerlines of drivable regions and intersection locations in an often unstructured and unmarked environment and enables the vehicle to focus its attention on regions that require detailed analysis. Existing methods of building such maps require manu- ally driving vehicles for collecting sensor measurements. Instead of pursuing a labor-intensive approach, we analyze an aerial image of a parking lot to build a topological map. In particular, our algorithm produces a lane-graph of a parking lot's drivable regions by executing several image processing steps. First it estimates drivable regions by superimposing detection of a parking spots onto the parking lot boundary segmentation. Second, a distance transform is applied to drivable regions to reveal its skeleton. Lastly our algorithm searches the distance map to identify a set of the peak points and connects them to generate a lane-graph that concisely represents drivable regions. Experiments show promising results of real-world parking lot aerial-imagery analysis.

In this paper we present algorithms for automatically generating a road network description from aerial imagery. The road network inforamtion (RNI) produced by our algorithm includes a composite topoloigical and spatial representation of the roads visible in an aerial image. We generate this data for use by autonomous vehicles operating on-road in urban environments. This information is used by the vehicles to both route plan and determine appropriate tactical behaviors. RNI can provide important contextual cues that influence driving behaviors, such as the curvature of the road ahead, the location of traffic signals, or pedestrian dense areas. The value of RNI was demonstrated compellingly in the DARPA Urban Challenge, where the vehicles relied on this information to drive quickly, safely and efficiently.

In this paper we introduce focused A* heuristic recomputation (FAHR), an enhancement to A* search that can detect and correct large discrepancies between the heuristic cost-to-go estimate and the true cost function. In situations where these large discrepancies exist, the search may expend significant effort escaping from the ¿bowl¿ of a local minimum. A* typically computes supporting data structures for the heuristic once, prior to initiating the search. FAHR directs the search out of the bowl by recomputing parts of the heuristic function opportunistically as the search space is explored. FAHR may be used when the heuristic function is in the form of a pattern database. We demonstrate the effectiveness of the algorithm through experiments on a ground vehicle path planning simulation.

Road network information (RNI) simplifies autonomous driving by providing strong priors about driving environments. Its usefulness has been demonstrated in the DARPA Urban Challenge. However, the need to manually generate RNI prevents us from fully exploiting its benefits. We envision an aerial image analysis system that automatically generates RNI for a route between two urban locations. As a step toward this goal, we present an algorithm that extracts the structure of a parking lot visible in an aerial image. We formulate this task as a problem of parking spot detection because extracting parking lot structures is closely related to detecting all of the parking spots. To minimize human intervention in use of aerial imagery, we devise a self-supervised learning algorithm that automatically obtains a set of canonical parking spot templates to learn the appearance of a parking lot and estimates the structure of the parking lot from the learned model. The data set extracted from a single image alone is too small to sufficiently learn an accurate parking spot model. To remedy this insufficient positive data problem, we utilize self-supervised parking spots obtained from other aerial images as prior information and a regularization technique to avoid an overfitting solution.

This paper describes the obstacle detection and tracking algorithms developed for Boss, which is Carnegie Mellon University 's winning entry in the 2007 DARPA Urban Challenge. We describe the tracking subsystem and show how it functions in the context of the larger perception system. The tracking subsystem gives the robot the ability to understand complex scenarios of urban driving to safely operate in the proximity of other vehicles. The tracking system fuses sensor data from more than a dozen sensors with additional information about the environment to generate a coherent situational model. A novel multiple-model approach is used to track the objects based on the quality of the sensor data. Finally, the architecture of the tracking subsystem explicitly abstracts each of the levels of processing. The subsystem can easily be extended by adding new sensors and validation algorithms.

Knowledge of the driving environment is essential for robotic vehicles to comply with traffic rules while autonomously traversing intersections. However, due to limited sensing coverage and continuous changes in driving conditions, rigidly-mounted sensors may not guarantee coverage of all regions of interest, all the time. Unobserved regions around intersections increase uncertainty in driving conditions. This paper describes a dynamic sensor planning method that searches for the optimal angles of two pointable sensors to maximally cover relevant unobserved regions of interest. The obtained angles are used to adjust the orientations of the pointable sensors and hence reduce uncertainty around intersections. Simulation results show that the sensor planning method increases the percentage of covered area. In addition to the sensor pointing problem, we provide an initial discussion of how to reason about occlusions in an urban environment. Occlusions caused by structures and other environmental features can increase uncertainty in driving environments. An occlusion handling method is used to detect these occlusions and enable our vehicle to model the presence of occluded regions. An awareness of occlusions enables safer driving decisions.

This paper discusses swerving as an alternative for stopping as a primary obstacle avoidance maneuver. By swerving instead of stopping, we show it is possible to achieve significantly higher-speeds given the same sensor capability. We have demonstrated navigation at speeds which rely on the principles described in this paper

This paper presents an approach that achieves robust high-speed navigation of prescribed routes. We present the mechatronic and software considerations that resulted in two robots that have completed over 4000 km of autonomous navigation of trails and off-road terrain at an average speed of approximately 30 km/h, with sustained top speeds of 55 km/h

The Atacama Desert of northern Chile may be the most lifeless place on Earth, yet where the desert meets the Pacific coastal range desiccation-tolerant micro-organisms are known to exist. The gradient of biodiversity and habitats in the Atacama’s subregions remain unexplored and are the focus of the Life in the Atacama project. To conduct this investigation, long traverses must be made across the desert with instruments for geologic and biologic measurements. In this paper we motivate the Life in the Atacama project from both astrobiologic and robotic perspectives. We focus on some of the research challenges we are facing to enable endurance navigation, resource cognizance, and long-term survivability. We conducted our first scientific investigation and technical experiments in Chile with the mobile robot Hyperion. We describe the experiments and the results of our analysis. These results give us insight into the design of an effective robotic astrobiologist and into the methods by which we will conduct scientific investigation in the next field season.

This paper presents several modifications to the basic rapidly-exploring random tree (RRT) search algorithm. The fundamental idea is to utilize a heuristic quality function to guide the search. Results from a relevant simulation experiment illustrate the benefit and drawbacks of the developed algorithms. The paper concludes with several promising directions for future research.

Sun-synchronous exploration is accomplished by reasoning about sunlight: where the Sun is in the sky, where and when shadows will fall, and how much power can be obtained through various courses of action. In July 2001 a solar-powered rover, named Hyperion, completed two sun-synchronous exploration experiments in the Canadian high arctic (75/spl deg/N). Using knowledge of orbital mechanics, local terrain, and expected power consumption, Hyperion planned a sun-synchronous route to visit designated sites while obtaining the necessary solar power for continuous 24-hour operation. Hyperion executed its plan and returned to its starting location with batteries fully charged after traveling more than 6 kilometers in barren, Mars-analog terrain. We describe the concept of sun-synchronous exploration. We overview the design of the robot Hyperion and the software system that enables it to operate sun-synchronously. We then discuss results from analysis of our first experiment in sun-synchronous exploration and conclude with observations.

This paper describes the navigation system used on a prototype sun-synchronous robot. Sun-synchrony is a concept that will enable exploration missions by solar-powered rovers that could last months or years. This paper presents the navigation algorithms developed for traversing natural terrain robustly. The novel elements of this work are the refinements necessary to transform laboratory-demonstrated technologies into a form useful for robust, Sun-synchronous exploration. Results of afield experiment in the Canadian Arctic, where the robot traversed 6.1km, 90% autonomously, are also presented.

Future space facilities that could power our planet and expand our horizons will differ vastly from the satellites and space stations familiar today. Characterized by their immense size and the difficulties of human construction in orbit, future space facilities will be assembled in part by robots. This paper profiles Skyworker, a prototype assembly, inspection, and maintenance (AIM) robot designed for large mass payload transport and assembly tasks. Skyworker is an attached mobile manipulator (AMM) capable of walking and working on the structure it is building.