The aim of this work was to position a Mobile Robot in an Intelligent Space, and this paper presents a sensorial system for measuring differential phase-shifts in a sinusoidally modulated infrared signal transmitted from the robot. Differential distances were obtained from these phase-shifts, and the position of the robot was estimated by hyperbolic trilateration. Due to the extremely severe trade-off between SNR, angle (coverage) and real-time response, a very accurate design and device selection was required to achieve good precision with wide coverage and acceptable robot speed. An I/Q demodulator was used to measure phases with one-stage synchronous demodulation to DC. A complete set of results from real measurements, both for distance and position estimations, is provided to demonstrate the validity of the system proposed, comparing it with other similar indoor positioning systems.

Terrestrial Radar Interferometry (TRI) is a measurement technique capable of measuring displacements with high temporal resolution at high accuracy. Current implementations of TRI use large and/or movable antennas for generating two-dimensional displacement maps. Multiple Input Multiple Output Synthetic Aperture Radar (MIMO-SAR) systems are an emerging alternative. As they have no moving parts, they are more easily deployable and cost-effective. These features suggest the potential usage of MIMO-SAR interferometry for structural health monitoring (SHM) supplementing classical geodetic and mechanical measurement systems. The effects impacting the performance of MIMO-SAR systems are, however, not yet sufficiently well understood for practical applications. In this paper, we present an experimental investigation of a MIMO-SAR system originally devised for automotive sensing, and assess its capabilities for deformation monitoring. The acquisitions generated for these investigations feature a 180${}^{\circ }$ Field-of-View (FOV), distances of up to 60 m and a temporal sampling rate of up to 400 Hz. Experiments include static and dynamic setups carried out in a lab-environment and under more challenging meteorological conditions featuring sunshine, fog, and cloud-cover. The experiments highlight the capabilities and limitations of the radar, while allowing quantification of the measurement uncertainties, whose sources and impacts we discuss. We demonstrate that, under sufficiently stable meteorological conditions with humidity variations smaller than 1%, displacements as low as 25 μm can be detected reliably. Detecting displacements occurring over longer time frames is limited by the uncertainty induced by changes in the refractive index.

Time-of-flight depth cameras are interesting sensors for contact-less 3-D metrology because they combine mechanical robustness with independence of ambient lighting conditions. Their actual performance depends on many factors and is hard to predict from data sheets. In this study, we investigate the deviations of the distance measurements of a high-end phase-based depth camera. We focus on the impact of: 1) self-warming and external temperature; 2) on range noise as a function of distance and acquisition time; and 3) on distance-dependent biases. We present the dedicated experimental setups comprising a climate chamber, a calibration bench with a reference interferometer, and a laser tracker that provides controlled conditions and ground-truth data. These setups allow investigating the absolute accuracy and mitigating repeatable distance biases by adapting the measurement model based on experimental data. For demonstration, we apply the investigation to two state-of-the-art industrial depth cameras of the same brand and type (Helios Lucid), showing significantly different response to external temperature but similar distance-dependent biases. We adapt the measurement model of one of the cameras for distance-dependent interpixel biases and demonstrate that the resulting parameters reduce also the distance biases of the other camera by about 80% to less than 1 mm at ranges of up to 1 m. This indicates the potential for batch error compensation. This article contributes to better understanding distance deviations of depth cameras and to improving the accuracy of such cameras.

Hyperspectral LiDAR enables non-contact mapping of the 3D surface geometry of an object along with its spectral reflectance signature and has proved to be effective for automated point cloud segmentation in various remote sensing applications. The established hyperspectral LiDAR methods offer a range precision of a few mm to a few cm for distances exceeding several meters. We propose a novel approach to hyperspectral LiDAR scanning based on a supercontinuum (SC) coherently broadened from a 780 nm frequency comb. It provides high precision distance measurements along with target reflectance over the 570–970 nm range of the SC output. The distance measurements are carried out by monitoring the differential phase delay of intermode beat notes generated by direct photodetection, while the backscattered light spectrum is acquired using a commercial CCD spectrometer with 0.16 nm resolution across the 400 nm bandwidth of the SC output. We demonstrate a measurement precision below 0.1 mm for a stand-off range up to 50 m on a diffuse target with around 89% reflectance. The measured relative accuracy as compared to a reference interferometer is on the order of 10⁻⁵ for distances up to 50 m. Initial results also indicate spectrum-based material classification within a 3D point cloud using a linear support vector machine. The results highlight the potential of this approach for joint high-precision laser scanning and automated material classification.

A method for infrared and cameras sensor fusion, applied to indoor positioning in intelligent spaces, is proposed in this work. The fused position is obtained with a maximum likelihood estimator from infrared and camera independent observations. Specific models are proposed for variance propagation from infrared and camera observations (phase shifts and image respectively) to their respective position estimates and to the final fused estimation. Model simulations are compared with real measurements in a setup designed to validate the system. The difference between theoretical prediction and real measurements is between $0.4$ cm (fusion) and $2.5$ cm (camera), within a 95% confidence margin. The positioning precision is in the cm level (sub-cm level can be achieved at most tested positions) in a $4\times 3$ m locating cell with 5 infrared detectors on the ceiling and one single camera, at distances from target up to 5 m and 7 m respectively. Due to the low cost system design and the results observed, the system is expected to be feasible and scalable to large real spaces.