Two-stage visual localisation: Landmark-based pose initialisation and model-based pose refinement

We show that landmark based localisation (LBL) and Lowe's model-based localisation (MBL) are complementary in that LBL provides a pose initialisation to MBL, which is a necessary input to the algorithm, and MBL can then refine that pose to a give more accurate pose estimate than LBL alone can provide. For LBL, we extend Betke and Gurvit's method, such that it can be used with standard perspective cameras (their original proposal was for omnidirectional cameras) in order to get a useful initial value as an input to Lowe's method. Intensive experiments have been carried out to analyse how camera parameters (intrinsic and extrinsic) affect the LBL position and orientation errors in the initial pose estimate. In error propagation experiments, we show that the position and orientation of a robot are sensitive to focal length and errors in imaged feature positions respectively. In the MBL pose refinement phase, we find that MBL is able to refine the position estimate, but the error in orientation estimate remains the same.