Salient Bundle Adjustment for Visual SLAM
Ke Wang, Sai Ma, Junlan Chen, Jianbo Lu

TL;DR
This paper introduces Salient Bundle Adjustment, a novel approach that weights feature points by saliency in visual SLAM, improving accuracy by emphasizing scene-important features, validated on KITTI and EuRoc datasets.
Contribution
It proposes a saliency-based weighting scheme for bundle adjustment in SLAM, integrating scene semantic and geometric info for enhanced performance.
Findings
Outperforms existing SLAM algorithms in accuracy
Effective in both indoor and outdoor environments
Provides open-source dataset and code for future research
Abstract
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…
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Taxonomy
TopicsVisual Attention and Saliency Detection · Robotics and Sensor-Based Localization · Advanced Image and Video Retrieval Techniques
