In this letter, we propose an end-to-end deep architecture that jointly learns to detect
obstacles and estimate their depth for MAV flight applications. Most of the existing …

A robust obstacle avoidance control program was developed for a mobile robot in the
context of tight, dynamic indoor environments. Deep Learning was applied in order to …

Recently, unmanned aerial vehicles (UAVs) have found extensive indoor applications. In
numerous indoor UAV scenarios, navigation paths remain consistent. While many indoor …

An accurate perception with a rapid response is fundamental for any autonomous vehicle to
navigate safely. Light detection and ranging (LiDAR) sensors provide an accurate estimation …

Mobile robots may be deployed in indoor environments for numerous tasks such as object
manipulation, search and rescue, and infrastructure mapping. To be safely deployed in …

We present our third prototype sensor and a localization method for Automated Guided
Vehicles (AGVs), for which small imaging LIght Detection and Ranging (LIDAR) and fusion …

This paper addresses the problem of mobile robot navigation in unknown corridors using
RGB-Depth cameras. Instead of building a full and global 3D map of the environment, the …

Simultaneous Localization and Mapping (SLAM) research has reached a level of maturity
enabling systems to build autonomously an accurate sparse map of the environment while …

Object recognition and depth perception are two tightly coupled tasks that are indispensable
for situational awareness. Most autonomous systems are able to perform these tasks by …