Monte Carlo Localization for Humanoid Robot Navigation in Complex Indoor Environments

Armin Hornung, Stefan Oßwald, Daniel Maier, Maren Bennewitz
2014 International Journal of Humanoid Robotics  
Accurate and reliable localization is a prerequisite for autonomously performing highlevel tasks with humanoid robots. In this article, we present a probabilistic localization method for humanoid robots navigating in arbitrary complex indoor environments using only onboard sensing, which is a challenging task. Inaccurate motion execution of biped robots leads to an uncertain estimate of odometry, and their limited payload constrains perception to observations from lightweight and typically
more » ... sensors. Additionally, humanoids do not walk on flat ground only and perform a swaying motion while walking, which requires estimating a full 6D torso pose. We apply Monte Carlo localization to globally determine and track a humanoid's 6D pose in a given 3D world model, which may contain multiple levels and staircases. We present an observation model to integrate range measurements from a laser scanner or a depth camera as well as attitude data and information from the joint encoders. To increase the localization accuracy, e.g., while climbing stairs, we propose a further observation model and additionally use monocular vision data in an improved proposal distribution. We demonstrate the effectiveness of our methods in extensive real-world experiments with a Nao humanoid. As the experiments illustrate, the robot is able to globally localize itself and accurately track its 6D pose while walking and climbing stairs.
doi:10.1142/s0219843614410023 fatcat:phooukthhrgpxlci2ffgg3wb4i