Posts tagged Kumar Shaurya Shankar
RaD-VIO: Rangefinder-aided Downward Visual-Inertial Odometry

Aerial vehicles require a reliable means of determining how they move for robust flight. A downward looking camera is often used to provide an estimate of the robots motion along the ground. In this work we employ the approximation of the ground underneath as being locally planar and utilize this property in concert with onboard inertial sensors for measuring attitude and a single laser beam based rangefinder to provide fast, robust odometry estimates. Specifically, we include all the sensor information within a unified filtering framework. The focus in this work is to provide a reliable, fast, and robust estimate of odometry that can serve ably as a backup for more sophisticated optimization based odometry algorithms.

Read More
Fast Monte-Carlo Localization on Aerial Vehicles using Approximate Continuous Belief Representations

A robot or a team of robots operating in large known environments require accurate knowledge of their exact location in the environment, in order to execute complex tasks. Limited size, weight and power constraints lead to constraints on the on-board computational capacity of aerial robots. This work presents a Monte-Carlo based real-time localization framework capable of running on such robots, enabled by using a compressed representation of the environment point cloud.

Read More