【摘要】 回环检测对于视觉同步定位和建图(visual simultaneous localization and mapping, VSLAM)系统减小累计误差和重定位具有重要意义。为缩短回环检测在线运行时间，同时满足准确率召回率需求，提出了一种基于宽度自编码器的快速回环检测算法(fast loop closure detection-broad autoencoder, FLCD-BA)。该检测算法改进了宽度学习网络，通过无监督的方式从输入数据中自主学习数据特征，进而运用于回环检测任务。与传统的深度学习方法不同，该网络使用伪逆的岭回归算法求解权重矩阵，通过增量学习的方法实现网络的快速重构，从而避免了整个网络的重复训练。所提算法在三个公开数据集上进行了实验，无须使用GPU设备，且网络的训练时间相比词袋模型以及深度学习的方法有较大缩短。实验结果表明该算法在检测回环时具有较高的准确率和召回率，测试中每帧的平均运行时间仅需21 ms,为视觉SLAM系统的回环检测提供了一种新算法。更多还原

【Abstract】 Loop closure detection is important for VSLAM systems to reduce cumulative errors and perform re-localization.In order to shorten the loop closure detection online runtime while meeting the accuracy recall rate requirement, the paper proposed a fast loop closure detection algorithm using broad autoencoder(FLCD-BA).The algorithm improved broad learning architecture to learn feature representations autonomously from the input data in an unsupervised manner, and then accomplished loop closure detection tasks.Differing from the traditional deep learning methods, the network used a pseudo-inverse ridge regression algorithm to solve the weight matrix and achieved fast reconfiguration of the network by incremental learning without retraining the entire network.The proposed algorithm experimented on three public datasets without using GPU devices, and the training time of the network greatly reduced compared to bag-of-words models as well as deep learning methods.The experimental results show that the algorithm has high accuracy and recall in detecting loop closure, and the average running time per frame in the test is only 21 ms, which provides a new algorithm for loop closure detection in visual SLAM systems.更多还原