6 accepted papers
In this paper, we present a low-latency odometry system designed for spinning lidars. Many existing lidar odometry methods wait for an entire sweep from the lidar before processing the data. This introduces a large delay between the first laser firing and its pose estimate. To reduce this latency, w…
In this work we investigate the problem of uncertainty estimation for image-guided depth completion. We extend Deep Basis Fitting (DBF) for depth completion within a Bayesian evidence framework to provide calibrated per-pixel variance. The DBF approach frames the depth completion problem in terms of…
Transferability of adversarial examples is of central importance for attacking an unknown model, which facilitates adversarial attacks in more practical scenarios, e.g., blackbox attacks. Existing transferable attacks tend to craft adversarial examples by indiscriminately distorting features to degr…
In this letter we propose a tightly-coupled Extended Kalman Filter framework for IMU-only state estimation. Strap-down IMU measurements provide relative state estimates based on IMU kinematic motion model. However the integration of measurements is sensitive to sensor bias and noise, causing signifi
Autonomous flight through unknown environments in the presence of obstacles is a challenging problem for micro aerial vehicles (MAVs). A majority of the current state-of-art research assumes obstacles as opaque objects that can be easily sensed by optical sensors such as cameras or LiDARs. However i…
Micro Aerial Vehicles have the potential to assist humans in real life tasks involving applications such as smart homes, search and rescue, and architecture construction. To enhance autonomous navigation capabilities these vehicles need to be able to create dense 3D maps of the environment, while co…