14 accepted papers
Collaborative planning under operational constraints is an essential capability for heterogeneous robot teams tackling complex large-scale real-world tasks. Unmanned Aerial Vehicles (UAVs) offer rapid environmental coverage, but flight time is often limited by energy constraints, whereas Unmanned Gr…
Relative pose estimation is crucial for coordinated multi-robot navigation. However, robots in close proximity often face intra-team occlusions, where teammates partially block each other's field of view, while dynamic environments further introduce environmental occlusions. Classical relative pose …
Coordinated multi-robot navigation is essential for robots to operate as a team in diverse environments. During navigation, robot teams usually need to maintain specific formations, such as circular formations to protect human teammates at the center. However, in complex scenarios such as narrow c…
Autonomous navigation in unstructured environments is a challenging task due to the complex and dynamic nature of robot-terrain interactions. Existing approaches often struggle to generalize amidst the complexities of real-world settings. They tend to rely on hand-engineered, rule-based robot models…
When robots operate in real-world off-road environments with unstructured terrains, the ability to adapt their navigational policy is critical for effective and safe navigation. However, off-road terrains introduce several challenges to robot navigation, including dynamic obstacles and terrain uncer…
Terrain adaptation is a critical ability for a ground robot to effectively traverse unstructured off-road terrain in real-world field environments such as forests. However, the expected or planned maneuvering behaviors cannot always be accurately executed due to setbacks such as reduced tire pressur…
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…
Traditional imitation learning provides a set of methods and algorithms to learn a reward function or policy from expert demonstrations. Learning from demonstration has been shown to be advantageous for navigation tasks as it allows for machine learning non-experts to quickly provide information nee…
Research in autonomous driving has benefited from a number of visual datasets collected from mobile platforms, leading to improved visual perception, greater scene understanding, and ultimately higher intelligence. However, this set of existing data collectively represents only highly structured, ur…
When a mobile robot is deployed in a field environment, e.g., during a disaster response application, the capability of adapting its navigational behaviors to unstructured terrains is essential for effective and safe robot navigation. In this paper, we introduce a novel joint terrain representation…
When working alongside human collaborators in dynamic environments such as a disaster recovery, an unmanned ground vehicle (UGV) may require fast field adaptation to perform its duties or learn novel tasks. In disaster recovery situations, personnel and equipment are constrained, so training must be…
We introduce an unsupervised semantic scene labeling approach that continuously learns and adapts semantic models discovered within a data stream. While closely related to unsupervised video segmentation, our algorithm is not designed to be an early video processing strategy that produces coherent o…
Multi-concept visual classification is emerging as a common environment perception technique, with applications in autonomous mobile robot navigation. Supervised visual classifiers are typically trained with large sets of images, hand annotated by humans with region boundary outlines followed by lab…
Visual classifiers are part of many applications including surveillance, autonomous navigation and scene understanding. The raw data used to train these classifiers is abundant and easy to collect but lacks labels. Labels are necessary for training supervised classifiers, but the labeling process re…