8 accepted papers
This paper presents a reinforcement learning-based quadrotor navigation method that leverages efficient differentiable simulation, novel loss functions, and privileged information to navigate around large obstacles. Prior learning-based methods perform well in scenes that exhibit narrow obstacles, b…
This paper presents a methodology to predict metric depth from monocular RGB images and an inertial measurement unit (IMU). To enable collision avoidance during autonomous flight, prior works either leverage heavy sensors (e.g., LiDARs or stereo cameras) or data-intensive and domain-specific fine-tu…
This paper describes methodologies to estimate the collision probability, Euclidean distance and gradient between a robot and a surface, without explicitly constructing a free space representation. The robot is assumed to be an ellipsoid, which provides a tighter approximation for navigation in clut
This paper introduces the open-source framework, GIRA, which implements fundamental robotics algorithms for reconstruction, pose estimation, and occupancy modeling using compact generative models. Compactness enables perception in the large by ensuring that the perceptual models can be communicated…
This letter describes an incremental multimodal surface mapping methodology, which represents the environment as a continuous probabilistic model. This model enables high-resolution reconstruction while simultaneously compressing spatial and intensity point cloud data. The strategy employed in this
This letter presents a continuous probabilistic modeling methodology for spatial point cloud data using finite Gaussian Mixture Models (GMMs) where the number of components are adapted based on the scene complexity. Few hierarchical and adaptive methods have been proposed to address the challenge of
This letter presents a framework for planning and perception for multi-robot exploration in large and unstructured three-dimensional environments. We employ a Gaussian mixture model for global mapping to model complex environment geometries while maintaining a small memory footprint which enables di
This paper develops an exploration framework that leverages Gaussian mixture models (GMMs) for high-fidelity perceptual modeling and exploits the compactness of the distributions for information sharing in communications-constrained applications. State-of-the-art, high-resolution perceptual modeling…