Mobile robot localization with GNSS multipath detection using pseudorange residuals

TL;DR

The paper addresses urban GNSS localization where NLOS multipath causes large errors. It introduces a map-free particle-filter approach that detects NLOS signals from pseudorange residuals and uses the subset of LOS measurements to compute a GNSS position solution, weighting particles by the Mahalanobis distance $D_i$ between the particle and the LOS-based solution. The method demonstrates meter-scale accuracy and fast convergence in real urban canyon experiments, without requiring external sensors or 3D maps. This approach has practical implications for robust mobile-robot localization in challenging urban environments.

Abstract

This paper proposes a novel positioning technique suitable for use in mobile robots in urban environments in which large global navigation satellite system (GNSS) positioning errors occur because of multipath signals. During GNSS positioning, the GNSS satellites that are obstructed by buildings emit reflection and diffraction signals, which are called non-line-of-sight (NLOS) multipath signals. These multipath signals cause major positioning errors. The key concept considered in this paper is the estimation of a user's position using the likelihood of the position hypotheses computed from the GNSS pseudoranges, consisting only of LOS signals based on the analysis of the pseudorange residuals. To determine the NLOS GNSS signals from the pseudorange residuals at the user's position, it is necessary to accurately determine the position before the computation of the pseudorange residuals. This problem is solved using a particle filter. We propose a likelihood estimation method using the Mahalanobis distance between the hypotheses of the user's position computed from only the LOS pseudoranges and the particles. To confirm the effectiveness of the proposed technique, a positioning test was performed in a real-world urban environment. The results demonstrated that the proposed method is effective for accurately estimating the user's position in urban canyons.

Figures (14)

• Figure 1: LOS and NLOS multipath signals in urban environments. NLOS multipath significantly affects the positioning accuracy.
• Figure 2: Overview of the proposed positioning method using a particle filter. NLOS GNSS signals are eliminated based on pseudorange residuals.
• Figure 3: Test course of the kinematic experiment in an urban canyon to evaluate the proposed positioning accuracy.
• Figure 4: Fish-eye image of the test environment. The environment is surrounded by tall buildings.
• Figure 5: Pseudorange residuals computed at the true position. Red lines indicate the thresholds to eliminate the large pseudorange residuals as NLOS signals.