Legolas: Deep Leg-Inertial Odometry

Abstract

Estimating odometry, where an accumulating position and rotation is tracked, has critical applications in many areas of robotics as a form of state estimation such as in SLAM, navigation, and controls. During deployment of a legged robot, a vision system's tracking can easily get lost. Instead, using only the onboard leg and inertial sensor for odometry is a promising alternative. Previous methods in estimating leg-inertial odometry require analytical modeling or collecting high-quality real-world trajectories to train a model. Analytical modeling is specific to each robot, requires manual fine-tuning, and doesn't always capture real-world phenomena such as slippage. Previous work learning legged odometry still relies on collecting real-world data, this has been shown to not perform well out of distribution. In this work, we show that it is possible to estimate the odometry of a legged robot without any analytical modeling or real-world data collection. In this paper, we present Legolas, the first method that accurately estimates odometry in a purely data-driven fashion for quadruped robots. We deploy our method on two real-world quadruped robots in both indoor and outdoor environments. In the indoor scenes, our proposed method accomplishes a relative pose error that is 73% less than an analytical filtering-based approach and 87.5% less than a real-world behavioral cloning approach. More results are available at: learned-odom.github.io