  This paper proposes a simple yet effective approach to learn visual features
online for improving loop-closure detection and place recognition, based on
bag-of-words frameworks. The approach learns a codeword in bag-of-words model
from a pair of matched features from two consecutive frames, such that the
codeword has temporally-derived perspective invariance to camera motion. The
learning algorithm is efficient: the binary descriptor is generated from the
mean image patch, and the mask is learned based on discriminative projection by
minimizing the intra-class distances among the learned feature and the two
original features. A codeword for bag-of-words models is generated by packaging
the learned descriptor and mask, with a masked Hamming distance defined to
measure the distance between two codewords. The geometric properties of the
learned codewords are then mathematically justified. In addition, hypothesis
constraints are imposed through temporal consistency in matched codewords,
which improves precision. The approach, integrated in an incremental
bag-of-words system, is validated on multiple benchmark data sets and compared
to state-of-the-art methods. Experiments demonstrate improved precision/recall
outperforming state of the art with little loss in runtime.

Computer Vision and Pattern Recognition