Conference Paper

Reconstruction of a road by local image matches and global 3D optimization

Comput. Vision Lab., Maryland Univ., College Park, MD
DOI: 10.1109/ROBOT.1990.126186 In proceeding of: Robotics and Automation, 1990. Proceedings., 1990 IEEE International Conference on
Source: IEEE Xplore

ABSTRACT A method is presented for reconstructing a 3-D road from a single
image. It finds the images of opposite points of the road. Opposite
points are points which face each other on the opposite sides of the
road; the images of these points are called matching points. For points
chosen from one side of the road image, the algorithm finds all the
matching point candidates on the other side, based on local properties
of a road. However, these solutions do not necessarily satisfy the
global properties of a typical road. A dynamic programming algorithm is
applied to reject the candidates which do not fit the global road. A
benchmark using synthetic roads is described. It shows that the roads
reconstructed by the proposed method match the actual roads better than
those reconstructed by two other road reconstruction algorithms.
Experiments with 50 road images taken by the autonomous land vehicle
(ALV) showed that the method is robust with real-world data and that the
reconstructions are fairly consistent with road profiles obtained by
fusion between range images and video images

1 Bookmark
 · 
49 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: In this paper, we describe our recent efforts on symbolic representations enabling autonomous driving in dynamic environments such as on-road driving. In contrast to traditional bottom-up approaches where temporal accumulation of locally insignificant inaccuracies causes eventual failures in high-level scene interpretation, our proposed knowledge-driven top-down approach combined with vehicle's intentions can provide valuable information to guide low-level bottom-up tasks and vice-versa. We contend that such rich symbolic representations can reduce the burden on sensory processing thereby dynamically directing it to look for particular features in expected locations and subsequently facilitating the vehicle to better react to potentially dangerous situations, such as the appearance of pedestrians in the road. We demonstrate the proposed approaches in various scenarios pertaining to vehicle perception and control using field data obtained from a military unmanned ground vehicle (UGV) traversing urban environments
    Ninth International Conference on Control, Automation, Robotics and Vision, ICARCV 2006, Singapore, 5-8 December 2006, Proceedings; 01/2006
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: In this paper, we describe our recent efforts in grouping sensory data into meaningful entities. Our grouping philosophy is based on perceptual organization principles using gestalt hypotheses where we impose structural regularity on sensory primitives stemming from a common underlying cause. We present results using field data from UGVs and outline the utility of our research in object recognition and tracking for autonomous vehicle navigation. In addition, we show how the grouping efforts can be useful for constructing symbolic topological maps when data from different sensing modalities are fused in a bottom-up and top-down fashion
    Applied Imagery and Pattern Recognition Workshop, 2005. Proceedings. 34th; 11/2005
  • [Show abstract] [Hide abstract]
    ABSTRACT: Our previous work on road detection for autonomous road vehicles suggests the usage of high-level symbolic knowledge about the road structure. In this paper, we present our new approach to symbolic road recognition. We explain feature extraction, model representation, and the tree search-based matching processes and discuss performance evaluation results.
    35th Applied Image Pattern Recognition Workshop (AIPR 2006), 11-13 October 2006, Washington, DC, USA, Proceedings; 01/2006

Full-text (2 Sources)

View
0 Downloads
Available from