<title>Landmark-based navigation: model extension and refinement</title>

摘要

Least-squares and robust methods were presented for determining the location and orientation of a mobile robot from visual measurements of modeled 3-D landmarks. However, building the 3-D landmark models is a time consuming and tedious process. For landmark-based navigation methods to be widely applicable, automatic methods have to be developed to build new 3-D models and enhance the existing models. Ideally, a robot would continuously build and update its world model as it explores the environment. This paper presents techniques to determine the 3-D location of image features from a sequence of monocular 2-D images captured by a camera mounted on the robot. The approach adopted here is to first build a partial model (possibly noisy) either manually, by stereo, or by tracking and reconstructing shallow structures over a sequence of images using the constraint of affine trackability. This model is subsequently used to compute the pose that relates the model coordinate system and the camera coordinate system of the image frames in the sequence. The unmodeled 3-D features (those not already in the model) are tracked over the image sequence and their 3-D locations recovered by a pseudo-triangulation process, a form of `induced stereo.' The triangulation process is also used to make new 3-D measurements of the initial model points. These measurements are then fused with the previous estimates to refine the set of initial model points.