论文标题
ORCVIO：对象残留受约束的视觉惯性探测仪
OrcVIO: Object residual constrained Visual-Inertial Odometry
论文作者
论文摘要
将对象级的语义信息引入同时本地化和映射（SLAM）系统至关重要。它不仅可以提高性能，还可以启用根据有意义的对象指定的任务。这项工作介绍了ORCVIO，用于视觉惯性探光仪与结构化对象模型的跟踪和优化紧密相结合。 ORCVIO通过语义特征和边界盒重新投入错误区分了对象的姿势和形状的批处理优化。估计的对象状态有助于对IMU相机状态的实时增量优化。使用实际数据评估ORCVIO进行准确的轨迹估计和大规模对象级映射的能力。
Introducing object-level semantic information into simultaneous localization and mapping (SLAM) system is critical. It not only improves the performance but also enables tasks specified in terms of meaningful objects. This work presents OrcVIO, for visual-inertial odometry tightly coupled with tracking and optimization over structured object models. OrcVIO differentiates through semantic feature and bounding-box reprojection errors to perform batch optimization over the pose and shape of objects. The estimated object states aid in real-time incremental optimization over the IMU-camera states. The ability of OrcVIO for accurate trajectory estimation and large-scale object-level mapping is evaluated using real data.
