  Recently, the philosophy of visual saliency and attention has started to gain
popularity in the robotics community. Therefore, this paper aims to mimic this
mechanism in SLAM framework by using saliency prediction model. Comparing with
traditional SLAM that treated all feature points as equal important in
optimization process, we think that the salient feature points should play more
important role in optimization process. Therefore, we proposed a saliency model
to predict the saliency map, which can capture both scene semantic and
geometric information. Then, we proposed Salient Bundle Adjustment by using the
value of saliency map as the weight of the feature points in traditional Bundle
Adjustment approach. Exhaustive experiments conducted with the state-of-the-art
algorithm in KITTI and EuRoc datasets show that our proposed algorithm
outperforms existing algorithms in both indoor and outdoor environments.
Finally, we will make our saliency dataset and relevant source code open-source
for enabling future research.
