A Benchmark of Absolute and Relative Positioning Solutions in GNSS Denied Environments

Precise positioning is fundamental to the Internet of Things (IoT) that delivers insights into everything from large-scale business to ordinary smart life. Accurate localization and positioning in global navigation satellite system (GNSS) denied environments, such as indoor-, underground-spaces, and forests, is one of the most prosperous research fields because of the great complexity prompted by various challenging application scenarios. Different sensors, algorithms, and combinations of those have been developed in past decades, which provided a great variety of possible solutions that deliver different positioning accuracies. However, a rigorous evaluation of the positioning accuracy of different mainstream solutions is missing, mainly because of the difficulties in acquiring reliable ground truth for referencing and the lack of comparable test/application conditions. A comprehensive benchmarking was carried out in this study based on the comparisons of six solutions that consist of different combinations of five positioning technologies, i.e., 1) ultrawideband (UWB) and inertial measurement unit (IMU); 2) UWB, IMU, and camera; 3) UWB and light detection and ranging (LIDAR); 4) UWB and radio detection and ranging (RADAR); 5) IMU, camera, and LIDAR; and 6) UWB, IMU, camera and LIDAR. The five technologies, i.e., UWB, IMU, camera, RADAR, and LIDAR, were commonly regarded as those that are with high applicability, accuracy, and robustness. New anchors self-positioning algorithm and integrity monitoring algorithm were proposed to further aid the compared solutions and the benchmark. High-precision survey (millimeter)-level ground truth references were acquired at indoor and outdoor test locations and applied in the evaluations, to assist reliable quantitive benchmarks about the positioning accuracies and stabilities of the compared solutions. The strengths, limitations, and potentials of each solution were analyzed. It was revealed that all relative positioning solutions accumulate positioning errors over time. Such accumulation was of the highest significance for RADAR, followed by camera. LIDAR is presented to be the most robust solution for relative positioning. Compared to camera, LIDAR, and RADAR alone, the integration of different technologies clearly improved the performance. The tight coupling (TC) performed slightly superior to loose coupling, and the unscented Kalman filter with TC had a higher positioning accuracy in most cases.