基于BoW模型的RGB-D SLAM算法的运动轨迹估计

Motion Trajectory Estimation Based on RGB-D SLAM Algorithm with BoW Model

针对目前RGB-D SLAM算法特征匹配耗时高、在大规模场景中闭环检测效率低的问题,提出了基于词袋(Bag of Word,BoW)模型的RGB-D SLAM改进算法.通过BoW模型缩小每个特征的匹配范围,解决了暴力特征匹配算法的耗时问题;利用BoW模型对关键帧序列进行相似度度量,得到闭环候选帧并通过多层次筛选最终确定闭环帧从而实现闭环优化,解决了传统的闭环检测算法不能适用于大规模场景的问题.与传统的RGB-D SLAM算法的实验测试及对比分析结果表明:改进的RGB-D SLAM算法能使得相机轨迹估计实时性更好,定位更准确.

Simultaneous localization and mapping (SLAM) has planned an important role in the field of robot navigation, control, and planning, and has become one of the key techniques in intelligent robotics with the development of robotic industry. Moreover, SLAM algorithm based on RGB-D camera has been one of efficient vision SLAM algorithms due to its low price, portability, and measurement of pixels’ depth. However, most of RGB-D SLAM algorithms show poor performance in real-time and localization accuracy. Especially, the feature matching is time-consuming and the loop closure detection is inefficient in large-scale scene. Aiming at these shortcomings, this work proposes an improved RGB-D SLAM algorithm based on BoW model. In the proposed algorithm, every ORB feature is converted to the BoW feature vector representing its father node in the designated layer of BoW model that has the tree structure composed of visual features. Since the two features extracted from different frames belong to the same node, they are probably the right feature matching. Moreover, the matching range of each ORB feature is narrowed for accelerating the feature matching process. Meanwhile, BoW model can recognize which frame is similar with the current key frames via the comparison of BoW vectors. Thus, the loop closure candidates, which are filtered in multi-level for obtaining loop closure frame, are obtained via similarity test. Finally, only one accurate loop candidate is confirmed by continuous detecting as well as transform computing between loop candidates and current frame. It is shown from the comparison experiments and analyses between the improved RGB-D SLAM and the original RGB-D SLAM algorithm that the improved RGB-D SLAM can attain better real-time performance and localization accuracy in camera trajectory estimation.