Continuous Occupancy Mapping in Dynamic Environments Using Particles

Particle-based dynamic occupancy maps were proposed in recent years to model the obstacles in dynamic environments. Current particle-based maps describe the occupancy status in discrete grid form and suffer from the grid size problem, wherein a large grid size is unfavorable for motion planning while a small grid size lowers efficiency and...

RAST: Risk-Aware Spatio-Temporal Safety Corridors for MAV Navigation in Dynamic Uncertain Environments

Autonomous navigation of Micro Aerial Vehicles (MAVs) in dynamic and unknown environments is a complex and challenging task. Current works rely on assumptions to solve the problem. The MAV's pose is precisely known, the dynamic obstacles can be explicitly segmented from static ones, their number is known and fixed, or they can be modeled with...

Stereo Visual Inertial Odometry for Robots with Limited Computational Resources*

Current existing stereo visual odometry algorithms are computationally too expensive for robots with restricted resources. Executing these algorithms on such robots leads to a low frame rate and unacceptable decay in accuracy. We modify S-MSCKF, one of the most computationally efficient stereo Visual Inertial Odometry (VIO) algorithm, to improve...

A Computationally Efficient Moving Horizon Estimator for Ultra-Wideband Localization on Small Quadrotors

We present a computationally efficient moving horizon estimator that allows for real-time localization using Ultra-Wideband measurements on small quadrotors. The estimator uses only a single iteration of a simple gradient descent method to optimize the state estimate based on past measurements, while using random sample consensus to reject...

Evolved Neuromorphic Control for High Speed Divergence-Based Landings of MAVs

Flying insects are capable of vision-based navigation in cluttered environments, reliably avoiding obstacles through fast and agile maneuvers, while being very efficient in the processing of visual stimuli. Meanwhile, autonomous micro air vehicles still lag far behind their biological counterparts, displaying inferior performance at a much...

Unsupervised Tuning of Filter Parameters Without Ground-Truth Applied to Aerial Robots

Autonomous robots heavily rely on well-tuned state estimation filters for successful control. This letter presents a novel automatic tuning strategy for learning filter parameters by minimizing the innovation, i.e., the discrepancy between expected and received signals from all sensors. The optimization process only requires the inputs and...