  Visual localization for planar moving robot is important to various indoor
service robotic applications. To handle the textureless areas and frequent
human activities in indoor environments, a novel robust visual localization
algorithm which leverages dense correspondence and sparse depth for planar
moving robot is proposed. The key component is a minimal solution which
computes the absolute camera pose with one 3D-2D correspondence and one 2D-2D
correspondence. The advantages are obvious in two aspects. First, the
robustness is enhanced as the sample set for pose estimation is maximal by
utilizing all correspondences with or without depth. Second, no extra effort
for dense map construction is required to exploit dense correspondences for
handling textureless and repetitive texture scenes. That is meaningful as
building a dense map is computational expensive especially in large scale.
Moreover, a probabilistic analysis among different solutions is presented and
an automatic solution selection mechanism is designed to maximize the success
rate by selecting appropriate solutions in different environmental
characteristics. Finally, a complete visual localization pipeline considering
situations from the perspective of correspondence and depth density is
summarized and validated on both simulation and public real-world indoor
localization dataset. The code is released on github.
