High level landmark-based visual navigation using unsupervised geometric constraints in local bundle adjustment

We present a high level landmark-based visual navigation approach for a monocular mobile robot. We utilize heterogeneous features, such as points, line segments, lines, planes, and vanishing points, and their inner geometric constraints as the integrated high level landmarks. This is managed through a multilayer feature graph (MFG). Our method extends local bundle adjustment (LBA)-based framework by explicitly exploiting different features and their geometric relationships in an unsupervised manner. The algorithm takes a video stream as input, initializes and incrementally updates MFG based on extracted key frames; it also refines localization and MFG landmarks through the LBA. Physical experiments show that our method can reduce the absolute trajectory error of a traditional point landmark-based LBA method by up to 63.9%.