We present a real-time stereo visual-inertial-SLAM system which is able to recover from complicated kidnap scenarios and failures online in realtime. We propose to learn the whole-image-descriptor in a weakly supervised manner based on NetVLAD and decoupled convolutions. We analyze the training difficulties in using standard loss formulations and propose an allpairloss and show its effect through extensive experiments. Compared to standard NetVLAD, our network takes an order of magnitude fewer computations and model parameters, as a result runs about three times faster. We evaluate the representation power of our descriptor on standard datasets with precision–recall. Unlike previous loop detection methods which have been evaluated only on fronto-parallel revisits, we evaluate the performance of our method with competing methods on scenarios involving large viewpoint difference. Finally, we present the fully functional system with relative computation and handling of multiple world co-ordinate system which is able to reduce odometry drift, recover from complicated kidnap scenarios and random odometry failures. We open source our fully functional system as an add-on for the popular VINS-Fusion.

我们提出了一种实时立体视觉惯性 SLAM 系统，该系统能够实时从复杂的绑架场景和故障中恢复。我们建议以基于 NetVLAD 和解耦卷积的弱监督方式学习整个图像描述符。我们分析了使用标准损失公式的训练困难，并提出了 allpairloss 并通过大量实验展示了其效果。与标准 NetVLAD 相比，我们的网络需要的计算和模型参数少一个数量级，因此运行速度大约快三倍。我们使用精确召回率评估我们的描述符在标准数据集上的表示能力。与之前仅在正面平行重访中评估的循环检测方法不同，我们在涉及大视点差异的场景中使用竞争方法评估我们的方法的性能。最后，我们提出了具有相对计算和处理多个世界坐标系的全功能系统，该系统能够减少里程计漂移，从复杂的绑架场景和随机里程计故障中恢复。我们将功能齐全的系统作为流行的 VINS-Fusion 的附件开源。