Localization and Mapping for Service Robots: Bearing-Only SLAM with an Omnicam

Abstract

The conducted experiments on a real platform prove that EKF-based bearing-only SLAM methods can be applied to features extracted from an omnicam image. In a first step, we successfully used artificial landmarks for the principal investigation of the performance of EKF-based bearing-only SLAM. However, service robotics applications ask for approaches that do not require any modifications of the environment. The next step was to introduce SIFT features into a bearing-only SLAM framework. We kept the idea of only estimating 2D poses of landmarks. This significantly reduces the overall complexity in terms of processing power since the state space of the Kalman filter is smaller and since the observation model is much simpler compared to 3D landmark poses. In particular the latest improvement exploiting the local neighbourhood of a SIFT feature shows stable performance in everyday indoor environments without requiring any modifications of the environment. The approach performed even under largely varying lighting conditions. Thus, the proposed approach successfully addresses the aspect of suitability for daily use as mandatory in service robotics.