ALVINN (Autonomous Land Vehicle In a Neural Network) is a 3-layer back-propagation network designed for the task of road following. Currently ALVINN takes images from a camera and a laser range finder as input and produces as output the direction the vehicle should travel in order to follow the road. Training has been conducted using simulated road images. Successful tests on the Carnegie Mellon autonomous navigation test vehicle indicate that the network can effectively follow real roads under certain field conditions. The representation developed to perform the task differs dramatically when the network is trained under various conditions, suggesting the possibility of a novel adaptive autonomous navigation system capable of tailoring its processing to the conditions at hand.

/pdf/alvinn-an-autonomous-land-vehicle-in-a-neural-network-1s0w97pkpv.pdf

ALVINN: An Autonomous Land Vehicle in a Neural Network

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.

/pdf/vision-for-mobile-robot-navigation-a-survey-4hb3uw9u9w.pdf

Vision for mobile robot navigation: a survey

We study the problem of perceiving forest or mountain trails from a single monocular image acquired from the viewpoint of a robot traveling on the trail itself. Previous literature focused on trail segmentation, and used low-level features such as image saliency or appearance contrast; we propose a different approach based on a deep neural network used as a supervised image classifier. By operating on the whole image at once, our system outputs the main direction of the trail compared to the viewing direction. Qualitative and quantitative results computed on a large real-world dataset (which we provide for download) show that our approach outperforms alternatives, and yields an accuracy comparable to the accuracy of humans that are tested on the same image classification task. Preliminary results on using this information for quadrotor control in unseen trails are reported. To the best of our knowledge, this is the first letter that describes an approach to perceive forest trials, which is demonstrated on a quadrotor micro aerial vehicle.

https://www.zora.uzh.ch/id/eprint/125475/1/RAL16_Giusti.pdf

A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots

We present a robust and real time approach to lane marker detection in urban streets. It is based on generating a top view of the road, filtering using selective oriented Gaussian filters, using RANSAC line fitting to give initial guesses to a new and fast RANSAC algorithm for fitting Bezier Splines, which is then followed by a post-processing step. Our algorithm can detect all lanes in still images of the street in various conditions, while operating at a rate of 50 Hz and achieving comparable results to previous techniques.

Real time Detection of Lane Markers in Urban Streets

A forward-facing vision system for a vehicle includes a forward-facing camera disposed in a windshield electronics module attached at a windshield of the vehicle and viewing through the windshield. A control includes a processor that, responsive to processing of captured image data, detects taillights of leading vehicles during nighttime conditions and, responsive to processing of captured image data, detects lane markers on a road being traveled by the vehicle. The control, responsive to lane marker detection and a determination that the vehicle is drifting out of a traffic lane, may control a steering system of the vehicle to mitigate such drifting, with the steering system manually controllable by a driver of the vehicle irrespective of control by the control. The processor, based at least in part on detection of lane markers via processing of captured image data, determines curvature of the road being traveled by the vehicle.

Vision system for vehicle

An efficient method for guiding high speed land vehicles along roadways by computer vision has been developed and demonstrated with image sequence processing hardware in a real-time simulation loop 1 . The approach is tailored to a well structured highway environment with good lanemarkings. Contour correlation and high order world models are the basic elements of the method, realised in a special multi-microprocessor (on board) computer system. Perspec-tive projection and dynamical models (Kalman filter) are used in an integrated approach for the design of the visual feedback control system. By determining road curvature explicity from the visual input, previously encountered steady state errors in curves are eliminated. The performance of the system will be demonstrated by a video film. The operation of the image sequence processing system has been tested on a typical Autobahn-scene at velocities up to 100 km/h.

E. D. Dickmanns

Papers

A Curvature-based Scheme for Improving Road Vehicle Guidance by Computer Vision