We present improvements to Kimera, an open-source metric-semantic visual-inertial SLAM library. In particular, we enhance Kimera-VIO, the visual-inertial odometry pipeline powering Kimera, to support better feature tracking, more efficient keyframe selection, and various input modalities (eg monocular, stereo, and RGB-D images, as well as wheel odometry). Additionally, Kimera-RPGO and Kimera-PGMO, Kimera's pose-graph optimization backends, are updated to support modern outlier rejection methods - specifically, Graduated-Non-Convexity - for improved robustness to spurious loop closures. These new features are evaluated extensively on a variety of simulated and real robotic platforms, including drones, quadrupeds, wheeled robots, and simulated self-driving cars. We present comparisons against several state-of-the-art visual-inertial SLAM pipelines and discuss strengths and weaknesses of the new release of Kimera. The newly added features have been released open-source at https://github.com/MIT-SPARK/Kimera.

翻译：本文提出了对开源度量-语义视觉惯性SLAM库Kimera的改进。具体而言，我们增强了驱动Kimera的视觉惯性里程计管线Kimera-VIO，以支持更优的特征跟踪、更高效的关键帧选择以及多种输入模式（例如单目、双目和RGB-D图像以及轮式里程计）。此外，Kimera的后端位姿图优化组件Kimera-RPGO和Kimera-PGMO已进行更新，支持现代外点剔除方法——特别是渐进非凸性算法——以提高对虚假闭环检测的鲁棒性。这些新特性在多种模拟和真实机器人平台上进行了广泛评估，包括无人机、四足机器人、轮式机器人以及模拟自动驾驶汽车。我们与多种最先进的视觉惯性SLAM管线进行了对比，并讨论了新版本Kimera的优势与不足。新增功能已在https://github.com/MIT-SPARK/Kimera 上开源发布。