论文标题
不确定性意识到紧密耦合的GPS fused lio-slam
Uncertainty-Aware Tightly-Coupled GPS Fused LIO-SLAM
论文作者
论文摘要
交付机器人旨在获得高精度以促进完整的自主权。需要一个精确的人行行周围环境的三维点云图来估计自我位置。有或没有循环结束方法，由于传感器漂移，较大的城市或城市地图映射后累积误差逐渐增加。因此，使用漂移或未对准的地图存在很高的风险。本文提出了一种融合GPS更新3D点云并消除累积错误的技术。提出的方法与其他现有方法显示了定量比较和定性评估的出色结果。
Delivery robots aim to achieve high precision to facilitate complete autonomy. A precise three-dimensional point cloud map of sidewalk surroundings is required to estimate self-location. With or without the loop closing method, the cumulative error increases gradually after mapping for larger urban or city maps due to sensor drift. Therefore, there is a high risk of using the drifted or misaligned map. This article presented a technique for fusing GPS to update the 3D point cloud and eliminate cumulative error. The proposed method shows outstanding results in quantitative comparison and qualitative evaluation with other existing methods.
