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The Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals (SHERLOC) is a robotic arm-mounted instrument on NASA’s Perseverance rover. SHERLOC has two primary boresights. The Spectroscopy boresight generates spatially resolved chemical maps using fluorescence and Raman spectroscopy coupled to microscopic images (10.1 μm/pixel). The second boresight is a Wide Angle Topographic Sensor for Operations and eNgineering (WATSON); a copy of the Mars Science Laboratory (MSL) Mars Hand Lens Imager (MAHLI) that obtains color images from microscopic scales (∼13 μm/pixel) to infinity. SHERLOC Spectroscopy focuses a 40 μs pulsed deep UV neon-copper laser (248.6 nm), to a ∼100 μm spot on a target at a working distance of ∼48 mm. Fluorescence emissions from organics, and Raman scattered photons from organics and minerals, are spectrally resolved with a single diffractive grating spectrograph with a spectral range of 250 to ∼370 nm. Because the fluorescence and Raman regions are naturally separated with deep UV excitation (<250 nm), the Raman region ∼ 800 – 4000 cm−1 (250 to 273 nm) and the fluorescence region (274 to ∼370 nm) are acquired simultaneously without time gating or additional mechanisms. SHERLOC science begins by using an Autofocus Context Imager (ACI) to obtain target focus and acquire 10.1 μm/pixel greyscale images. Chemical maps of organic and mineral signatures are acquired by the orchestration of an internal scanning mirror that moves the focused laser spot across discrete points on the target surface where spectra are captured on the spectrometer detector. ACI images and chemical maps (< 100 μm/mapping pixel) will enable the first Mars in situ view of the spatial distribution and interaction between organics, minerals, and chemicals important to the assessment of potential biogenicity (containing CHNOPS). Single robotic arm placement chemical maps can cover areas up to 7x7 mm in area and, with the < 10 min acquisition time per map, larger mosaics are possible with arm movements. This microscopic view of the organic geochemistry of a target at the Perseverance field site, when combined with the other instruments, such as Mastcam-Z, PIXL, and SuperCam, will enable unprecedented analysis of geological materials for both scientific research and determination of which samples to collect and cache for Mars sample return.

Lightweight aerial laser scanning systems have become a practical alternative over the past few years for collecting 3D geospatial data, typically carried by drones. The positional accuracy expected for the collected data depends on the magnitude of errors generated by the sensors during data acquisition and data processing. These lightweight systems operate in kinematics mode, utilising less accurate attitude sensors compared to conventional aerial systems. Errors resulting from GNSS/IMU solutions (position and attitude) and boresight misalignment angles can significantly affect the accuracy of the point cloud. To reduce errors in platform positioning and other sources of error, a static calibration technique for lightweight systems is proposed in this paper. The technique is based on a static system assembly in a calibration field, using the displacement of a set of specific targets in the object space to simulate a flight path and significantly reduce errors from the positioning and attitude system. This is achieved by levelling the platform using the accelerometers by minimising accelerations in the X and Y axes. The misalignments of boresight angles are estimated based on observations at control points. A technique using the concept of Virtual Control Point (VCP) is also applied to reduce measurement errors. An experimental feasibility study was conducted using a system comprising an IbeoLux laser scanner and a NovAtel SPAN-IGM-S1 inertial measurement unit. The results showed that the technique is viable, but requires some improvements, mainly in using larger ranges. Improvements in boresight estimation were observed when using VCPs, compared to conventional calibration, especially in planimetry.

This study assesses the performance of UAV lidar system in measuring high-resolution snow depths in agro-forested landscapes in southern Québec, Canada. We used manmade, mobile ground control points in summer and winter surveys to assess the absolute vertical accuracy of the point cloud. Relative accuracy was determined by a repeat flight over one survey block. Estimated absolute and relative errors were within the expected accuracy of the lidar (~5 and ~7 cm, respectively). The validation of lidar-derived snow depths with ground-based measurements showed a good agreement, however with higher uncertainties observed in forested areas compared with open areas. A strip alignment procedure was used to attempt the correction of misalignment between overlapping flight strips. However, the significant improvement of inter-strip relative accuracy brought by this technique was at the cost of the absolute accuracy of the entire point cloud. This phenomenon was further confirmed by the degraded performance of the strip-aligned snow depths compared with ground-based measurements. This study shows that boresight calibrated point clouds without strip alignment are deemed to be adequate to provide centimeter-level accurate snow depth maps with UAV lidar. Moreover, this study provides some of the earliest snow depth mapping results in agro-forested landscapes based on UAV lidar.

This paper details a new automatic calibration method for the boresight angles between a LiDAR (Light Detection and Ranging) and an inertial measurement unit (IMU), based on a data selection algorithm, followed by the adjustment of boresight angles. This method, called LiDAR-IMU boresight automatic calibration (LIBAC), takes in input overlapping survey strips following simple line patterns over regular slopes. We first construct a boresight error observability criterion, used to select automatically the most sensitive points to boresight errors. From these points, we adjust the boresight angles. From a statistical analysis of the adjustment results, we derive the boresight angle precision. Results obtained with LIBAC on several LiDAR system integrated within drones are presented. We also give results about the reproducibility of the method.

Free space optical (FSO) communication is a promising candidate for the next generation (5G and beyond) wireless communication systems, due to its merits (i.e. low latency, high data rate, and license‐free band, among others). However, atmospheric turbulence (AT) as well as pointing error (PE) are two of the main challenges with FSO communication that affect its performance. Here, the exact closed‐form expression of the average secrecy capacity and secrecy outage probability under the composite effect of AT and non‐zero boresight PEs is evaluated. For all the regimes of the AT (weak to strong), a generalised Malaga distribution is used to model the channel fading gain of the FSO link. The expressions are generalised and valid for all turbulence, and are applicable for intensity modulation direct detection as well as heterodyne detection techniques.

This paper presents a high-performance dual-band radome composed of cyanate/quartz fiber composite and frequency-selective surfaces (FSS). The practical steps from FSS and radome segmented design to fabrication are presented. The transmission characteristics of the FSS plate and radome are measured using the free-space method, and the results show that the FSS achieves transmission in both Ku and Ka bands, with frequency points of 15.4 GHz in the Ku band and 33.8 GHz in the Ka band, which confirms the feasibility of the FSS and radome segmented design. After that, the electrical performance of the radome is measured. The results show that the power transmission coefficient of the radome is not less than 70% in the range of ± 40°, and the maximum value of the center frequency is greater than 85% in the Ku/Ka operating band under vertical and horizontal polarization. The boresight error (BSE) is less than 20.5′ within ± 20° of the scanning angle. These results confirm that this FSS structure has high value for application in a curved radome.

Mobile LiDAR Scanning (MLS) systems and UAV LiDAR Scanning (ULS) systems equipped with precise Global Navigation Satellite System (GNSS)/Inertial Measurement Unit (IMU) positioning units and LiDAR sensors are used at an increasing rate for the acquisition of high density and high accuracy point clouds because of their safety and efficiency. Without careful calibration of the boresight angles of the MLS systems and ULS systems, the accuracy of data acquired would degrade severely. This paper proposes an automatic boresight self-calibration method for the MLS systems and ULS systems using acquired multi-strip point clouds. The boresight angles of MLS systems and ULS systems are expressed in the direct geo-referencing equation and corrected by minimizing the misalignments between points scanned from different directions and different strips. Two datasets scanned by MLS systems and two datasets scanned by ULS systems were used to verify the proposed boresight calibration method. The experimental results show that the root mean square errors (RMSE) of misalignments between point correspondences of the four datasets after boresight calibration are 2.1 cm, 3.4 cm, 5.4 cm, and 6.1 cm, respectively, which are reduced by 59.6%, 75.4%, 78.0%, and 94.8% compared with those before boresight calibration.

In this paper, an efficient method based on volume integral equation is developed to analyze the effects of ablation of a radome on the boresight error. To avoid recalculating the whole impedance matrix when the permittivity of the radome or the shape of the top portion is slightly changed due to ablation, the radome is divided into unaffected and affected parts and the volume equivalent current instead of the displacement current is used as the unknown. This permits us to reassemble rather than recalculate the impedance matrix when the ablation condition is altered. Moreover, a viable preconditioning technique is introduced and integrated with the multilevel fast multipole algorithm (MLFMA) to cope with the electrically large antenna-radome system (ARS). Simulation results are provided for the boresight error (BSE) and boresight error slope (BSES) of the ARS at some different ablation states. The present approach is considerably faster than using the conventional methods.

In a UAV system, physical sensor offsets occur between an observation sensor and a GPS/IMU sensor which represents the position of UAV. The difference of angle between the GPS/IMU sensor’s axis and the observation sensor’s axis is referred to as boresight angle. The difference in physical position between the two is referred to as lever-arm. It is important to obtain accurate offset values in order to utilize UAVs in rapid mapping manner. Due to the sensor offsets, misalignment error can be caused when generating a mosaic image. Offset values can be measured by using extensive ground control points. However, this is very costly and time-consuming. In this study, we describe an estimation method for sensor offsets using tie-points and re-weighted least square estimation method. The proposed method consists of 5 steps. Firstly, a frame images for the target area were classified into image strips based on the kappa value of initial EOPs (Exterior Orientation Parameters), after which tie points were extracted between adjacent images. Secondly, strip bundle adjustment was performed to update initial EOPs using images with the same flight direction. Thirdly, tie-points between adjacent strips were extracted. Fourthly, block bundle adjustment was performed in all images, using all extracted tie-points. Finally, a mosaic image is generated using the EOPs value to which the estimated sensor offset is applied. From this study, we confirmed that the sensor offset of the platform could be estimated only with the tie-points extracted between adjacent images. And we confirmed that misalignment was adjusted when generating mosaic image. We expect that our research makes UAV system to be operated more fluently.

NASA’s OSIRIS-REx asteroid sample return mission spacecraft includes the Touch And Go Camera System (TAGCAMS) three camera-head instrument. The purpose of TAGCAMS is to provide imagery during the mission to facilitate navigation to the target asteroid, confirm acquisition of the asteroid sample, and document asteroid sample stowage. The cameras were designed and constructed by Malin Space Science Systems (MSSS) based on requirements developed by Lockheed Martin and NASA. All three of the cameras are mounted to the spacecraft nadir deck and provide images in the visible part of the spectrum, 400–700 nm. Two of the TAGCAMS cameras, NavCam 1 and NavCam 2, serve as fully redundant navigation cameras to support optical navigation and natural feature tracking. Their boresights are aligned in the nadir direction with small angular offsets for operational convenience. The third TAGCAMS camera, StowCam, provides imagery to assist with and confirm proper stowage of the asteroid sample. Its boresight is pointed at the OSIRIS-REx sample return capsule located on the spacecraft deck. All three cameras have at their heart a 2592 × 1944 pixel complementary metal oxide semiconductor (CMOS) detector array that provides up to 12-bit pixel depth. All cameras also share the same lens design and a camera field of view of roughly 44 ∘ × 32 ∘ with a pixel scale of 0.28 mrad/pixel. The StowCam lens is focused to image features on the spacecraft deck, while both NavCam lens focus positions are optimized for imaging at infinity. A brief description of the TAGCAMS instrument and how it is used to support critical OSIRIS-REx operations is provided.