of a large number of features before stopping, so we do not test Hoeffding’s inequality experimentally. In the next section, we propose a number of rules that show better performance.

EXPERIMENTAL RESULTS {#sec:results}
====================

In this section, we compare the performance of the original BoW method with the BoW method that uses our stopping rules, when applied to the appearance-based localization problem. We will describe the datasets we used for performance evaluation of both methods, followed by discussion of our experimental results. We also describe the different stopping rules that we used in our experiments.

Datasets
--------

We performed our experiments on four datasets. Two datasets have been selected from the Oxford City Center dataset and two from the New College dataset [@fabmap] (see Figure \[fig:datasets\]). Both have been used as the benchmark for localization and SLAM evaluations. The ground truth data is also available for each of them[^5]. Each dataset contains two sets of image sequences. One sequence is taken from the right camera and the other from the left camera mounted on the robot. Each sequence of the City Center dataset contains $1237$ images and each of the New College contains $1073$. The resolution of images is $640\times480$. 