Exploring Performance Bounds of Visual Place Recognition Using Extended Precision

Recent advances in image description and matching allowed significant improvements in Visual Place Recognition (VPR). The wide variety of methods proposed so far and the increase of the interest in the field have rendered the problem of evaluating VPR methods an important task. As part of the localization process, VPR is a critical stage for many robotic applications and it is expected to perform reliably in any location of the operating environment. To design more reliable and effective localization systems this letter presents a generic evaluation framework based on the new Extended Precision performance metric for VPR. The proposed framework allows assessment of the upper and lower bounds of VPR performance and finds statistically significant performance differences between VPR methods. The proposed evaluation method is used to assess several state-of-the-art techniques with a variety of imaging conditions that an autonomous navigation system commonly encounters on long term runs. The results provide new insights into the behaviour of different VPR methods under varying conditions and help to decide which technique is more appropriate to the nature of the venture or the task assigned to an autonomous robot.