In anomaly detection, a prominent task is to induce a model to identify anomalies learned solely based on normal data.
We present Gradient-SDF, a novel representation for 3D geometry that combines the advantages of implict and explicit representations.
The square root formulation pervades three major aspects of our optimization-based sliding-window estimator: for bundle adjustment we eliminate landmark variables with nullspace projection; to store the marginalization prior we employ a matrix square root of the Hessian; and when marginalizing old poses we avoid forming normal equations and update the square root prior directly with a specialized QR decomposition.
Continuous-time trajectory representation has recently gained popularity for tasks where the fusion of high-frame-rate sensors and multiple unsynchronized devices is required.
We reconstruct a set of non-linear factors that make an optimal approximation of the information on the trajectory accumulated by VIO.
Neglecting the effects of rolling-shutter cameras for visual odometry (VO) severely degrades accuracy and robustness.
For trajectory evaluation, we also provide accurate pose ground truth from a motion capture system at high frequency (120 Hz) at the start and end of the sequences which we accurately aligned with the camera and IMU measurements.