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Aligning a robot's trajectory or map to the inertial frame is a criticalcapability that is often difficult to do accurately even though inertialmeasurement units (IMUs) can observe absolute roll and pitch with respect togravity. Accelerometer biases and scale factor errors from the IMU's initialcalibration are often the major source of inaccuracies when aligning therobot's odometry frame with the inertial frame, especially for low-grade IMUs.Practically, one would simultaneously estimate the true gravity vector,accelerometer biases, and scale factor to improve measurement quality but thesequantities are not observable unless the IMU is sufficiently excited. Whileseveral methods estimate accelerometer bias and gravity, they do not explicitlyaddress the observability issue nor do they estimate scale factor. We present afixed-lag factor-graph-based estimator to address both of these issues. Inaddition to estimating accelerometer scale factor, our method mitigates limitedobservability by optimizing over a time window an order of magnitude largerthan existing methods with significantly lower computational burden. Theproposed method, which estimates accelerometer intrinsics and gravityseparately from the other states, is enabled by a novel, velocity-agnosticmeasurement model for intrinsics and gravity, as well as a new method forgravity vector optimization on $S^2$. Accurate IMU state prediction,gravity-alignment, and roll/pitch drift correction are experimentallydemonstrated on public and self-collected datasets in diverse environments.

Joint On-Manifold Gravity and Accelerometer Intrinsics Estimation for Inertially Aligned Mapping