Quadruped robots are currently a widespread platform for robotics research, thanks to powerful controllers based on Reinforcement Learning and the availability of affordable and robust commercial platforms.

However, to broaden the adoption of the technology in the real world, we require robust navigation stacks relying only on low-cost sensors such as depth cameras.

This paper presents a first step towards a robust localization, mapping, and navigation system for low-cost quadruped robots. We combine contact-aided kinematic, visual-inertial odometry, and depth-stabilized vision, enhancing stability and accuracy of the system. 

Our results in simulation and two different real-world quadruped platforms show that our system can generate an accurate 2d map of the environment, robustly localize itself, and navigate autonomously.

System Structure

Scan Stabilization (3X Speedup)

Map creation with the Silver Bagdger can be visualized below in the AWS warehouse with (left) and without (right) Scan Stabilization. 

Leg Odometry

We use kinematic leg odometry for (i) resetting the Visual Odometry when it loses feature tracking (ii) to add a velocity constraint to the SLAM factor graph as seen in the figure below

Localization Results

We evaluate our system's ability to localize itself on two different environments accross 5 runs each. We use RTAB-Map [1] for the SLAM front-end and the ROS Slam Toolbox [2] for the slam back-end. Below is a table consisting of the evaluation metrics for each independent run.

Other Robot Trajectories

We test our system on three other robots in simulation: The Honey Badger (Silver Badger without spine joint), Unitree A1, and Unitree Go1. Below are the trajectories for one run of each robot in each of the AWS environments

AWS Warehouse

AWS House

Mapping results in simulation

Below are the maps from created while running our system on both simulated environments.

Navigation results (3X Speedup)

We evaluate the robot's navigation capabilities on a pre-mapped environment by sequentially sending 5 different poses to reach. Our system reliably attains every goal with minimal latency, whereas the baseline only reaches a subset of the targets—and then at substantially longer times.

AWS Warehouse

Unitree Go2

Silver Badger

AWS House

Unitree Go2

Silver Badger

Exploration results (2X Speedup)

This section illustrates the robots ability to autonomously explore an unknown environment while maintaining accurate localization and generating a high quality map that covers above 90% of the area. We use frontier-based exploration strategy using a nearest-frontier method [3].

Real Robot Experiments

Localization

Below is a depiction of all 4 trajectories, each with different system components, for one of the runs of the Silver Badger in the IAS and IRIM labs. It can be seen that the combination of leg odometry and scan stabilization is crucial to achieving accurate localization.

IAS Lab Trajectories

IRIM Lab Trajectories

Mapping

Cluttererd Environment

Cleaner Environment

Parameters for the observer

References

[1] M. Labbé and F. Michaud, “Rtab-map as an open-source lidar and visual simultaneous localization and mapping library for large-scale and long-term online operation,” Journal of field robotics, vol. 36, no. 2, pp. 416–446, 2019.

[2] S. Macenski and I. Jambrecic, “Slam toolbox: Slam for the dynamic world,” Journal of Open Source Software, 2021.

[3] B. Yamauchi, “A frontier-based approach for autonomous exploration,” in Proc. CIRA, 1997, pp. 146–151.