完全在线的双目直接法视觉SLAM算法

视觉SLAM在机器人的室外作业如野外探索、定位侦察中扮演了重要角色.为了使得机器人可以更好地进行室外作业,提出一种不受词袋模型的固定词汇限制的完全在线实时双目直接法视觉SLAM算法.作为直接法视觉SLAM,所提到的系统可以利用任何具有足够强度梯度的图像像素,使其在缺少特征点的区域仍具有很强的鲁棒性.在系统算法中引入双目静态残差约束并去除遮挡的滑窗优化来增强系统的跟踪精度,增加闭环检测和位姿图优化模块,并建立在线词袋模型,使得系统在大规模且陌生的环境中依然可以进行工作.将此算法在公开的EuRoC数据集和KITTI数据集上进行性能评估,结果表明,所提出的系统的定位精度优于最先进的直接法视觉SLAM系统,且室内场景和室外场景均具有鲁棒性.

Fully online stereo direct vision SLAM algorithm

Visual simultaneous localization and mapping(SLAM) plays an important role in the outdoor work of robots, such as field exploration, positioning and reconnaissance. In order to make the robots work better outdoors, this paper proposes a fully online real-time binocular direcnt vision SLAM algorithm which is not limited by the fixed vocabulary of the bag of word. As a direct vision SLAM, the system mentioned in this paper can make use of any image pixel with sufficient intensity gradient to make it still have strong robustness in the area lacking feature points. The system algorithm introduces binocular static residual constraint and sliding window optimization to remove occlusion to enhance the tracking accuracy of the system, adds closed-loop detection and pose map optimization modules, and establishes an online bag of word, so that the system can still work in a large-scale and unfamiliar environment. The performance of this algorithm is evaluated on the public EuRoC dataset and KITTI dataset. The positioning accuracy of the proposed system is better than the most advanced direct visual SLAM system, and is robust to indoor scenes and outdoor scenes.