Surveys the developments of the last 20 years in the area of vision for mobile robot navigation. Two major components of the paper deal with indoor navigation and outdoor navigation. For each component, we have further subdivided our treatment of the subject on the basis of structured and unstructured environments. For indoor robots in structured environments, we have dealt separately with the cases of geometrical and topological models of space. For unstructured environments, we have discussed the cases of navigation using optical flows, using methods from the appearance-based paradigm, and by recognition of specific objects in the environment.

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Vision for mobile robot navigation: a survey

A description is given of VITS (for vision task sequencer), the vision system for the autonomous land vehicle (ALV) Alvin, addressing in particular the task of road-following. The ALV vision system builds symbolic descriptions of road and obstacle boundaries using both video and range sensors. The authors discuss various road segmentation methods for video-based road-following, along with approaches to boundary extraction and transformation of boundaries in the image plane into a vehicle-centered three-dimensional scene model. >

VITS-a vision system for autonomous land vehicle navigation

In this paper the texture analysis methods being used at present are reviewed. Statistical as well as structural approaches are included and their performances are compared. Concerning the former approach, the gray level difference method, filter mask texture measures, Fourier power spectrum analysis, cooccurrence features, gray level run lengths, autocorrelation features, methods derived from texture models, relative extrema measures, and gray level profiles are discussed. Structural methods which describe texture by its primitives and some placement rules are treated as well. Attention has to be paid to some essential preprocessing steps and to the influence of rotation and scale on the texture analysis methods. Finally the problem of texture segmentation is briefly discussed.

Texture analysis Anno 1983

The DARPA PerceptOR program has implemented a rigorous evaluative test program which fosters the development of field relevant outdoor mobile robots. Autonomous ground vehicles were deployed on diverse test courses throughout the USA and quantitatively evaluated on such factors as autonomy level, waypoint acquisition, failure rate, speed, and communications bandwidth. Our efforts over the three year program have produced new approaches in planning, perception, localization, and control which have been driven by the quest for reliable operation in challenging environments. This paper focuses on some of the most unique aspects of the systems developed by the CMU PerceptOR team, the lessons learned during the effort, and the most immediate challenges that remain to be addressed.

Toward Reliable Off Road Autonomous Vehicles Operating in Challenging Environments

Most autonomous outdoor navigation systems tested on actual robots have centered on local navigation tasks such as avoiding obstacles or following roads. Global navigation has been limited to simple wandering, path tracking, straight-line goal seeking behaviors, or executing a sequence of scripted local behaviors. These capabilities are insufficient for unstructured and unknown environments, where replanning may be needed to account for new information discovered in every sensor image. To address these problems, the authors developed a complete system that integrates local and global navigation. The local system uses a scanning laser rangefinder to detect and avoid obstacles. The global system uses an incremental path planning algorithm to optimally replan the global path for each detected obstacle. A control arbiter steers the robot to achieve the proper balance between safety and goal acquisition. This system was tested on a real robot and successfully drove it 1.4 kilometers to find a goal given no a priori map of the environment.

/pdf/a-complete-navigation-system-for-goal-acquisition-in-unknown-4dmtxgj6ef.pdf

A complete navigation system for goal acquisition in unknown environments

This report describes range data based obstacle avoidance techniques developed for use on an autonomous road-following robot vehicle. The purpose of these techniques is to detect and locate obstacles present in a road environment for navigation of a robot vehicle equipped with an active laser-based range sensor. Techniques are presented for obstacle detection, obstacle location, and coordinate transformations needed in the construction of Scene Models (symbolic structures representing the 3-D obstacle boundaries used by the vehicle's Navigator for path planning). These techniques have been successfully tested on an outdoor robotic vehicle, the Autonomous Land Vehicle (ALV), at speeds up to 3.5 km/hour.

Obstacle Avoidance On Roadways Using Range Data

The Navigation system for Martin Marietta Denver Aerospace's Autonomous Land Vehicle project receives information from the Vision system about road boundaries and obstacle locations. This information is used in an optimization equation to create trajectory points on the road. This paper briefly describes the Vision processing, and describes the Navigation algorithms.

David G. Morgenthaler

Papers

Obstacle Avoidance On Roadways Using Range Data

Vision-based road following in the autonomous land vehicle

Two remarks on multidimensional texture analysis