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Precise and real-time localization of unmanned aerial vehicles (UAVs) orrobots in GNSS denied indoor environments are critically important for variouslogistics and surveillance applications. Vision-based simultaneously locatingand mapping (VSLAM) are key solutions but suffer location drifts intexture-less, man-made indoor environments. Line features are rich in man-madeenvironments which can be exploited to improve the localization robustness, butexisting point-line based VSLAM methods still lack accuracy and efficiency forthe representation of lines introducing unnecessary degrees of freedoms. Inthis paper, we propose Inverse Depth Line Localization(IDLL), which models eachextracted line feature using two inverse depth variables exploiting the factthat the projected pixel coordinates on the image plane are rather accurate,which partially restrict the lines. This freedom-reduced representation oflines enables easier line determination and faster convergence of bundleadjustment in each step, therefore achieves more accurate and more efficientframe-to-frame registration and frame-to-map registration using both point andline visual features. We redesign the whole front-end and back-end modules ofVSLAM using this line model. IDLL is extensively evaluated in multipleperceptually-challenging datasets. The results show it is more accurate,robust, and needs lower computational overhead than the currentstate-of-the-art of feature-based VSLAM methods.

IDLL: Inverse Depth Line based Visual Localization in Challenging Environments