基于图像与激光点云融合的移动机器人定位与建图算法。,Sensors

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基于图像与激光点云融合的移动机器人定位与建图算法。
Sensors ( IF 3.4 ) Pub Date : 2022-05-28 , DOI: 10.3390/s22114114
Jun Dai ₁ , Dongfang Li ₁ , Yanqin Li ₁ , Junwei Zhao ₁ , Wenbo Li ₁ , Gang Liu ₁

Affiliation

鉴于视觉SLAM（同时定位和映射）算法检测到的图像特征缺乏尺度信息，许多缺乏深度信息的特征的积累会导致尺度模糊，从而导致退化和跟踪失败。在本文中，我们引入激光雷达点云来为图像特征提供额外的深度信息，以估计自我运动以辅助视觉 SLAM。为了增强姿态估计的稳定性，改进了基于非线性优化的视觉SLAM前端。在帧间位姿估计中引入极点误差，根据特征点是否有深度信息计算残差。特征的残差重构目标函数并迭代求解机器人的位姿。基于关键帧的方法用于局部优化姿势以降低优化问题的复杂性。实验结果表明，改进后的算法在KITTI数据集和户外场景中取得了较好的效果。与纯视觉SLAM算法相比，移动机器人的轨迹误差降低了52.7%。本文提出的LV-SLAM算法在不同环境下具有良好的适应性和鲁棒稳定性。

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Mobile Robot Localization and Mapping Algorithm Based on the Fusion of Image and Laser Point Cloud.

Given the lack of scale information of the image features detected by the visual SLAM (simultaneous localization and mapping) algorithm, the accumulation of many features lacking depth information will cause scale blur, which will lead to degradation and tracking failure. In this paper, we introduce the lidar point cloud to provide additional depth information for the image features in estimating ego-motion to assist visual SLAM. To enhance the stability of the pose estimation, the front-end of visual SLAM based on nonlinear optimization is improved. The pole error is introduced in the pose estimation between frames, and the residuals are calculated according to whether the feature points have depth information. The residuals of features reconstruct the objective function and iteratively solve the robot's pose. A keyframe-based method is used to optimize the pose locally in reducing the complexity of the optimization problem. The experimental results show that the improved algorithm achieves better results in the KITTI dataset and outdoor scenes. Compared with the pure visual SLAM algorithm, the trajectory error of the mobile robot is reduced by 52.7%. The LV-SLAM algorithm proposed in this paper has good adaptability and robust stability in different environments.

更新日期：2022-05-28

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