Kimera2：真实世界中的鲁棒且精确的度量-语义SLAM
Kimera2: Robust and Accurate Metric-Semantic SLAM in the Real World
AI总结：
本文提出Kimera2，通过增强VIO特征跟踪、关键帧选择、多模态输入以及引入渐进非凸性外点剔除，显著提升了度量-语义SLAM在真实机器人平台上的鲁棒性与精度。
摘要：
我们介绍了对Kimera的改进，这是一个开源的度量-语义视觉-惯性SLAM库。具体来说，我们增强了Kimera-VIO，即为Kimera提供动力的视觉-惯性里程计流程，以支持更好的特征跟踪、更高效的关键帧选择以及多种输入模态（例如单目、立体和RGB-D图像，以及轮式里程计）。此外，Kimera的位姿图优化后端Kimera-RPGO和Kimera-PGMO已更新，以支持现代外点剔除方法——特别是渐进非凸性（Graduated-Non-Convexity）——以提高对虚假回环闭合的鲁棒性。这些新特性在各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、轮式机器人和模拟自动驾驶汽车。我们展示了与多个最先进的视觉-惯性SLAM流程的对比，并讨论了Kimera新版本的优缺点。新增功能已在https://github.com/MIT-SPARK/Kimera开源发布。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
