Showing papers by "David G. Lowe published in 2001"
21 May 2001
TL;DR: A vision-based mobile robot localization and mapping algorithm is described which uses scale-invariant image features as landmarks in unmodified dynamic environments which are localized and robot ego-motion is estimated by matching them, taking into account the feature viewpoint variation.
Abstract: A key component of a mobile robot system is the ability to localize itself accurately and build a map of the environment simultaneously. In this paper, a vision-based mobile robot localization and mapping algorithm is described which uses scale-invariant image features as landmarks in unmodified dynamic environments. These 3D landmarks are localized and robot ego-motion is estimated by matching them, taking into account the feature viewpoint variation. With our Triclops stereo vision system, experiments show that these features are robustly matched between views, 3D landmarks are tracked, robot pose is estimated and a 3D map is built.
590 citations
01 Dec 2001
TL;DR: This paper presents a method for combining multiple images of a 3D object into a single model representation that provides for recognition of 3D objects from any viewpoint, the generalization of models to non-rigid changes, and improved robustness through the combination of features acquired under a range of imaging conditions.
Abstract: There have been important recent advances in object recognition through the matching of invariant local image features. However, the existing approaches are based on matching to individual training images. This paper presents a method for combining multiple images of a 3D object into a single model representation. This provides for recognition of 3D objects from any viewpoint, the generalization of models to non-rigid changes, and improved robustness through the combination of features acquired under a range of imaging conditions. The decision of whether to cluster a training image into an existing view representation or to treat it as a new view is based on the geometric accuracy of the match to previous model views. A new probabilistic model is developed to reduce the false positive matches that would otherwise arise due to loosened geometric constraints on matching 3D and non-rigid models. A system has been developed based on these approaches that is able to robustly recognize 3D objects in cluttered natural images in sub-second times.
582 citations
29 Oct 2001
TL;DR: Experiments show that recognition of position within a map without any prior estimate can be achieved using the scale-invariant landmarks, and can localize itself globally based on the database containing landmarks of sufficient distinctiveness.
Abstract: Our mobile robot system uses scale-invariant visual landmarks to localize itself and build a 3D map of the environment simultaneously. As image features are not noise-free, we carry out error analysis and use Kalman filters to track the 3D landmarks, resulting in a database map with landmark positional uncertainty. By matching a set of landmarks as a whole, our robot can localize itself globally based on the database containing landmarks of sufficient distinctiveness. Experiments show that recognition of position within a map without any prior estimate can be achieved using the scale-invariant landmarks.
179 citations