This paper presents a framework for direct visual-LiDAR SLAM that combines the sparse depth measurement of light detection and ranging (LiDAR) with a monocular camera. The exploitation of the depth measurement between two sensor modalities has been reported in the literature but mostly by a keyframe-based approach or by using a dense depth map. When the sparsity becomes severe, the existing methods reveal limitation. The key finding of this paper is that the direct method is more robust under sparse depth with narrow field of view. The direct exploitation of sparse depth is achieved by implementing a joint optimization of each measurement under multiple keyframes. To ensure real-time performance, the keyframes of the sliding window are kept constant through rigorous marginalization. Through cross-validation, loop-closure achieves the robustness even in large-scale mapping. We intensively evaluated the proposed method using our own portable camera-LiDAR sensor system as well as the KITTI dataset. For the evaluation, the performance according to the LiDAR of sparsity was simulated by sampling the laser beam from 64 to 16 and 8. The experiment proves that the presented approach is significantly outperformed in terms of accuracy and robustness under sparse depth measurements.

中文翻译：

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DVL-SLAM：稀疏深度增强的直接视觉LiDAR SLAM

本文提出了一种直接视觉LiDAR SLAM的框架，该框架结合了单眼相机对光检测和测距的稀疏深度测量（LiDAR）。在文献中已经报道了在两种传感器模态之间进行深度测量的方法，但主要是通过基于关键帧的方法或通过使用密集的深度图。当稀疏性变得严重时，现有方法显示出局限性。本文的主要发现是直接在稀疏深度和狭窄视野下，该方法更加鲁棒。稀疏深度的直接开发是通过在多个关键帧下对每个测量进行联合优化来实现的。为了确保实时性能，滑动窗口的关键帧通过严格的边缘化保持恒定。通过交叉验证，闭环即​​使在大规模映射中也能实现鲁棒性。我们使用我们自己的便携式相机-LiDAR传感器系统以及KITTI数据集，对提议的方法进行了深入评估。为了进行评估，对稀疏LiDAR的性能进行了仿真，方法是对64到16和8的激光束进行采样。实验证明，在稀疏深度测量下，该方法在准确性和鲁棒性方面明显优于其他方法。