Orientation constraints for Wi-Fi SLAM using signal strength gradients
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Orientation constraints for Wi-Fi SLAM using signal strength gradients Hsiao-Chieh Yen1
· Chieh-Chih Wang2 · Cheng-Fu Chou1
Received: 21 July 2017 / Accepted: 9 June 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract We propose the signal strength gradient (SSG) orientation constraints for simultaneous localization and mapping (SLAM) using Wi-Fi received signal strength (RSS) measurements. We show that under certain circumstances, the relative orientation between nearby trajectory segments can be recovered from the cosine similarity between their SSGs. We then show how to obtain trajectory segments and self-consistent SSGs by jointly segmenting Wi-Fi measurements and odometry. Because SSG orientation constraints inevitably contain outliers, we also evaluate the effectiveness of robust SLAM backends on the proposed constraints. Experiments show that Wi-Fi SLAM using the proposed method can correctly estimate orientations given topologically incorrect initialization on trajectories with little to no overlapping sections. Keywords Wi-Fi SLAM · WiFi SLAM · Orientation · Robust pose graph SLAM
1 Introduction Localization using Wi-Fi signals has seen wide adoption during the past decade. It is particularly useful in indoor and urban environments where GPS is unavailable or unreliable, with common applications ranging from indoor navigation to asset tracking and advertisement. The current dominant approach of Wi-Fi localization, following the seminal work of (Bahl and Padmanabhan 2000), is to measure received signal strength (RSS) from surrounding access points (APs), and then compare the measurements against a previously collected RSS map. However, constructing an RSS map involves assigning each RSS measurement with a location label, which requires a significant amount of human labor. A large body of work has focused on automating the construction of Wi-Fi maps using simultaneous localization and mapping (SLAM) techniques. The main challenge in Wi-Fi SLAM is that Wi-Fi RSS measurements are highly noisy
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Hsiao-Chieh Yen [email protected] Chieh-Chih Wang [email protected] Cheng-Fu Chou [email protected]
1
Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
2
Department of Electrical and Computer Engineering, National Chiao Tung University, Hsinchu, Taiwan
and low-resolution, making RSS fingerprints a very unreliable feature for loop closure detection. Existing works on Wi-Fi SLAM sidestep this limitation by avoiding loop closure detection altogether. Instead, they look for the optimal trajectory satisfying spatial smoothness constraints of RSS measurements, often through nonlinear optimization. However, good initialization is required for such optimization to not fall into local minima, which is often unavailable without using strong priors or additional sensors. Furthermore, the spatial smoothness constraint entails that at room scale and along corridors, indoor environments are effectively onedimensional: if two trajecto
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