  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.
