13 accepted papers
We present Stylos, a single-forward 3D Gaussian framework for 3D style transfer that operates on unposed content, from a single image to a multi-view collection, conditioned on a separate reference style image. Stylos synthesizes a stylized 3D Gaussian scene without per-scene optimization or precomp…
Recent advances have improved autonomous navigation and mapping under payload constraints, but current multi-robot inspection algorithms are unsuitable for nano-drones, due to their need for heavy sensors and high computational resources. To address these challenges, we introduce <italic xmlns:mml="
We present a boundary-aware domain adaptation model for LiDAR scan full-scene semantic segmentation (LiDARNet). Our model can extract both the domain private features and the domain shared features with a two branch structure. We embedded Gated-SCNN into the segmentor component of LiDARNet to learn…
We present a method for path optimization for ground vehicles in off-road environments at high speeds. This path optimization considers the kinematic constraints of the vehicle. By thinking in the actuator space we can represent such constraints as limits in the space rather than derived properties…
Semantic scene understanding is crucial for robust and safe autonomous navigation, particularly so in off-road environments. Recent deep learning advances for 3D semantic segmentation rely heavily on large sets of training data, however existing autonomy datasets either represent urban environments…
Drivers and other road users often encounter situations (e.g., arriving at an intersection simultaneously) where priority is ambiguous or unclear but must be resolved via communication to reach agreement. This poses a challenge for autonomous vehicles, for which no direct means for expressing intent…
In this paper we perform an extensive experimental evaluation of three planar target based 3D-LIDAR camera calibration algorithms, on a sensor suite consisting multiple 3D-LIDARs and cameras, assessing their robustness to random initialization and by using metrics like Mean Line Re-projection Error…
In this paper, we present a fast, on-line mapping and planning solution for operation in unknown, off-road, environments. We combine obstacle detection along with a terrain gradient map to make simple and adaptable cost map. This map can be created and updated at 10 Hz. An A* planner finds optimal p…
Obstacle avoidance is a key feature for safe Unmanned Aerial Vehicle (UAV) navigation. While solutions have been proposed for static obstacle avoidance, systems enabling avoidance of dynamic objects, such as drones, are hard to implement due to the detection range and field-of-view (FOV) requirement…
In this paper, we describe the method and implementation of a real-time tumor tracking system for a pencil beam scanning (PBS) proton therapy system. PBS is an advanced cancer treatment system that can benefit from precise localization of the tumors through motion. We utilize techniques such as cros…
In this paper, we present a collaborative path-planning framework for a group of micro aerial vehicles that are capable of localizing through vision. Each of the micro aerial vehicles is assumed to be equipped with a forward facing monocular camera. The vehicles initially use their captured images t…
We present a framework for vision based localization for two or more multirotor aerial vehicles relative to each other. This collaborative localization technique is built upon a relative pose estimation strategy between two or more cameras with the capability of estimating accurate metric poses betw…
We present the design and implementation of an aircraft collision avoidance algorithm for Unmanned Aerial Vehicles (UAVs). Automatic Dependent Surveillance-Broadcast (ADS-B) is used to detect aircraft. The UAV needs to fly through pre-assigned waypoints while avoiding collisions with other aircraft.…