3 accepted papers
Anticipating the motion of dynamic objects is critical for making intelligent decisions navigating through an environment while avoiding collisions. In this work, we propose a CNN model that estimates 3D motion of objects using sequences of monocular images. We show that we can train this model with…
In this work we propose an unsupervised training method that finetunes a single image depth estimation CNN towards a new environment. The network, which has been pretrained on stereo data, only requires monocular input for finetuning. Unlike other unsupervised methods, it produces depth estimations…
This paper demonstrates the use of a single forward facing camera for obstacle avoidance on a quadrotor. We train a CNN for estimating depth from a single image. The depth map is then fed to a behaviour arbitration based control algorithm that steers the quadrotor away from obstacles. We conduct exp…