3D simultaneous localisation and map-building using active vision for a robot moving on undulating terrain

TLDR

Robots operating on uneven terrain require 3‑D SLAM rather than the usual 2‑D planar assumption. The authors present a real‑time EKF‑based SLAM system that enables unconstrained 3‑D localisation by modelling wheeled motion over unknown slopes. The system fuses fixating stereo vision, odometry, and roll/pitch accelerometer data to build a sparse map on the fly. The robot achieved accurate, repeatable localisation while traversing an undulating course by combining visual, odometry, and accelerometer measurements.

Abstract

Work in simultaneous localisation and map-building ("SLAM") for mobile robots has focused on the simplified case in which a robot is considered to move in two dimensions on a ground plane. While this is often a good approximation, a large number of real-world applications require robots to move around terrain which has significant slopes and undulations, and it is desirable that these robots too should be able to estimate their locations by building maps of natural features. We describe a real-time EKF-based-SLAM system permitting unconstrained 3D localisation, and in particular develop models for the motion of a wheeled robot in the presence of unknown slope variations. In a fully automatic implementation, our robot observes visual point features using fixating stereo vision and builds a sparse map on-the-fly. Combining this visual measurement with information from odometry and a roll/pitch accelerometer sensor, the robot performs accurate, repeatable localisation while traversing an undulating course.