4 accepted papers
Dynamic environments such as urban areas are still challenging for popular visual-inertial odometry (VIO) algorithms. Existing datasets typically fail to capture the dynamic nature of these environments, therefore making it difficult to quantitatively evaluate the robustness of existing VIO methods.
Decentralized deployment of drone swarms usually relies on inter-agent communication or visual markers that are mounted on the vehicles to simplify their mutual detection. This letter proposes a vision-based detection and tracking algorithm that enables groups of drones to navigate without communica
Decentralized drone swarms deployed today either rely on sharing of positions among agents or detecting swarm members with the help of visual markers. This letter proposes an entirely visual approach to coordinate markerless drone swarms based on imitation learning. Each agent is controlled by a sma
In this paper, we present a multi-sensory terrain classification algorithm with a generalized terrain representation using semantic and geometric features. We compute geometric features from lidar point clouds and extract pixel-wise semantic labels from a fully convolutional network that is trained…