Autonomously navigating robots need to perceive and interpret their surroundings.
Currently, cameras are among the most used sensors due to their high resolution and frame …

Perceiving and mapping the surroundings are essential for autonomous navigation in any
robotic platform. The algorithm class that enables accurate mapping while correcting the …

Autonomous drone racing competitions are a proxy to improve unmanned aerial vehicles'
perception, planning, and control skills. The recent emergence of autonomous nano-sized …

The use of Unmanned Aerial Vehicles (UAVs) is rapidly increasing in applications ranging
from surveillance and first-aid missions to industrial automation involving cooperation with …

Recent advances have improved autonomous navigation and mapping under payload
constraints, but current multi-robot inspection algorithms are unsuitable for nano-drones …

We present a new SLAM algorithm that utilizes an inexpensive four-point LiDAR to
supplement the limitations of the short-range and viewing angles of RGB-D cameras …

Autonomous drone racing competitions are a proxy to improve unmanned aerial vehicles'
perception, planning, and control skills. The recent emergence of autonomous nano-sized …

Detecting traversable regions from monocular vision is a fundamental problem for indoor
robot navigation. In existing works, this is typically formulated as the problem of …

We propose a graph SLAM algorithm for sparse range sensing that incorporates a soft
Manhattan world utilizing landmark-landmark constraints. Sparse range sensing is …

In the realm of autonomous indoor navigation for mobile robots, the fusion of Simultaneous
Localization and Mapping (SLAM) technologies has evolved into a vital pillar for achieving …