Lidar-based exploration and discretization for mobile robot planning

TL;DR

This paper introduces a lidar-based discretization method that converts continuous environment data into a graph of free polytopes for improved high-level planning and low-level control in robotic systems.

Contribution

It proposes a novel discretization algorithm that identifies free polytopes from lidar data and constructs a transition graph for integrated planning and control.

Findings

Effective environment discretization from lidar data

Successful implementation in drone and Segway experiments

Enhanced planning and control integration

Abstract

In robotic applications, the control, and actuation deal with a continuous description of the system and environment, while high-level planning usually works with a discrete description. This paper considers the problem of bridging the low-level control and high-level planning for robotic systems via sensor data. In particular, we propose a discretization algorithm that identifies free polytopes via lidar point cloud data. A transition graph is then constructed where each node corresponds to a free polytope and two nodes are connected with an edge if the two corresponding free polytopes intersect. Furthermore, a distance measure is associated with each edge, which allows for the assessment of quality (or cost) of the transition for high-level planning. For the low-level control, the free polytopes act as a convenient encoding of the environment and allow for the planning of collision-free trajectories that realizes the high-level plan. The results are demonstrated in high-fidelity ROS simulations and experiments with a drone and a Segway.