{"id":10458,"date":"2026-01-20T10:15:49","date_gmt":"2026-01-20T10:15:49","guid":{"rendered":"https:\/\/www.oxts.com\/?p=10458"},"modified":"2026-04-09T15:54:56","modified_gmt":"2026-04-09T15:54:56","slug":"your-blueprint-for-faster-better-georeferencing","status":"publish","type":"post","link":"https:\/\/www.oxts.com\/zh\/your-blueprint-for-faster-better-georeferencing\/","title":{"rendered":"\u66f4\u5feb\u3001\u66f4\u597d\u5730\u8fdb\u884c\u5730\u7406\u5750\u6807\u6d4b\u91cf\u7684\u84dd\u56fe"},"content":{"rendered":"\n<div class=\"conttitletext mwb-block py-4 lg:py-8\">\n        <div class=\"container md:grid md:grid-cols-12\">\n        <div class=\"col-span-6 col-start-5\">\n    \n           \n    \n            \n\n\n    \n\n    <h3 class=\"h3 mb-6\">From Data Capture to Competitive Edge<\/h3>\n\n            \n\n\n    \n\n    <h5 class=\"h5 mb-6\">When you\u2019re mapping complex environments you&#8217;re fighting a battle on two fronts. Your clients demand perfect data, as accurate as possible, and your bottom line demands that you work as efficiently as possible.<\/h5>\n\n\n        <div class=\"wysiwyg p\">\n            <p><span data-contrast=\"auto\">The pursuit of georeferencing perfection can kill efficiency, and leads to missed deadlines and cost overruns. But cutting corners to save time would destroy the accuracy, and credibility that your entire professional reputation is built on.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">In short, you need a way of collecting positioning data for georeferencing that\u2019s<\/span><span data-ccp-props=\"{&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<ul>\n<li aria-setsize=\"-1\" data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"7\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:765,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span data-contrast=\"auto\">\u00a0<\/span><span data-contrast=\"auto\">A<\/span><span data-contrast=\"auto\">ccurate<\/span><span data-ccp-props=\"{&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li aria-setsize=\"-1\" data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"7\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:765,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"2\" data-aria-level=\"1\"><span data-contrast=\"auto\">Reliable<\/span><span data-ccp-props=\"{&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li aria-setsize=\"-1\" data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"7\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:765,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"3\" data-aria-level=\"1\"><span data-contrast=\"auto\">W<\/span><span data-contrast=\"auto\">orks\u00a0<\/span><span data-contrast=\"auto\">right the first time <\/span><span data-ccp-props=\"{&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/li>\n<\/ul>\n<p><span data-contrast=\"auto\">At the same time, you need to streamline your back office work to turn that raw data into a highly precise georeferenced point cloud (or other kind of survey if you aren\u2019t using LiDAR).<\/span><\/p>\n<p><span data-contrast=\"auto\">The truth is, Ground Control Points and even basic GNSS\/INS systems just can&#8217;t keep up with these dual pressures anymore.<\/span><\/p>\n<p><span data-contrast=\"auto\">In this blog,\u00a0we\u2019re\u00a0showcasing\u00a0how <span style=\"color: #ca181c;\"><a style=\"color: #ca181c;\" href=\"https:\/\/www.oxts.com\/solutions\/inertial-navigation-solutions\/navigation-hardware\/\" target=\"_blank\" rel=\"noopener\">OXTS GNSS\/INS<\/a><\/span> devices help you manage the conflicting pressures of georeferencing work, through specialised features designed with surveyors like you in mind.\u00a0They\u2019re\u00a0accessed by activating\u00a0<\/span><b><span data-contrast=\"auto\">feature codes<\/span><\/b><span data-contrast=\"auto\"> on your device \u2013 so if you are an OXTS customer interested in levelling up your georeferencing process, <\/span><a href=\"https:\/\/www.oxts.com\/contact\/\" target=\"_blank\" rel=\"noopener\"><span data-contrast=\"none\">get in touch with us today<\/span><\/a><span data-contrast=\"auto\">.<\/span><span data-ccp-props=\"{&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n\n        <\/div>\n\n            <\/div>\n    <\/div>\n    \n            \n    <\/div>\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-8f761849 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:66.66%\">\n            \n<div class=\"contformembed mwb-block bg-light text-dark py-12 md:py-16\">\n    <div class=\"container grid md:grid-cols-3 gap-6 md:gap-6\">\n        <div class=\"col-span-1\">\n            \n                    <\/div>\n        \n        <div class=\"col-span-3\">\n            <div class=\"form-embed-container\">\n                <script charset=\"utf-8\" type=\"text\/javascript\" src=\"\/\/js.hsforms.net\/forms\/embed\/v2.js\"><\/script>\r\n<script>\r\n  hbspt.forms.create({\r\n    portalId: \"7624321\",\r\n    formId: \"7340f828-489d-4cfe-9c0c-28adaff646cd\",\r\n    region: \"na1\"\r\n  });\r\n<\/script>\n            <\/div>\n        <\/div>\n    <\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:33.33%\"><\/div>\n<\/div>\n\n\n\n<div class=\"conttitletext mwb-block py-4 lg:py-8\">\n        <div class=\"container md:grid md:grid-cols-12\">\n        <div class=\"col-span-6 col-start-5\">\n    \n           \n    \n            \n\n\n    \n\n\n            \n\n\n    \n\n\n\n        <div class=\"wysiwyg p\">\n            <h5><span style=\"color: #ca181c;\">Getting data you can trust<\/span><\/h5>\n<p><span data-contrast=\"auto\">Reliable fieldwork <\/span><span data-contrast=\"auto\">is <\/span><span data-contrast=\"auto\">the foundation upon which your entire final deliverable is built.<\/span><span data-contrast=\"auto\">\u00a0Although post-processing plays a vital role in optimising\u00a0<\/span><span data-contrast=\"auto\">your data, errors during the s<\/span><span data-contrast=\"auto\">urvey process can affect the data to the point where\u00a0there\u2019s\u00a0nothing to do but\u00a0<\/span><span data-contrast=\"auto\">re-survey \u2013 a costly and time-consuming misstep.<\/span><\/p>\n<p><span data-contrast=\"auto\">Our hardware and georeferencing features are designed to make sure the data you collect in the field is rock-solid from the start, building a foundation of integrity for your entire project.<\/span><\/p>\n\n        <\/div>\n\n            <\/div>\n    <\/div>\n    \n            \n    <\/div>\n\n<div class=\"wp-block-image\">\n<figure class=\"alignleft size-large is-resized\"><img decoding=\"async\" width=\"1024\" height=\"601\" src=\"https:\/\/www.oxts.com\/wp-content\/uploads\/2026\/01\/Urban4-1024x601.png\" alt=\"Georeferencing\" class=\"wp-image-10468\" style=\"aspect-ratio:1.7038961038961038;width:630px;height:auto\" srcset=\"https:\/\/www.oxts.com\/wp-content\/uploads\/2026\/01\/Urban4-1024x601.png 1024w, https:\/\/www.oxts.com\/wp-content\/uploads\/2026\/01\/Urban4-300x176.png 300w, https:\/\/www.oxts.com\/wp-content\/uploads\/2026\/01\/Urban4-768x451.png 768w, https:\/\/www.oxts.com\/wp-content\/uploads\/2026\/01\/Urban4-18x12.png 18w, https:\/\/www.oxts.com\/wp-content\/uploads\/2026\/01\/Urban4.png 1253w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\">Urban canyon georeferencing requires precision without consistent GNSS signal<\/figcaption><\/figure>\n<\/div>\n\n\n<div class=\"conttitletext mwb-block py-4 lg:py-8\">\n        <div class=\"container md:grid md:grid-cols-12\">\n        <div class=\"col-span-6 col-start-5\">\n    \n           \n    \n            \n\n\n    \n\n\n            \n\n\n    \n\n\n\n        <div class=\"wysiwyg p\">\n            <h5><span style=\"color: #ca181c;\">Sync every sensor with PTP\/GPTP<\/span><\/h5>\n<p><span data-contrast=\"auto\">Direct georeferencing is simple in theory: for every single\u00a0point\u00a0the LiDAR sees, you need to know exactly where the sensor was and how it was oriented at that exact\u00a0<\/span><span data-contrast=\"auto\">point in time<\/span><span data-contrast=\"auto\">. This is only possible if the clock on your INS and the clock on your LiDAR are as close to perfectly in sync as possible.<\/span><\/p>\n<p><span data-contrast=\"auto\">If\u00a0they&#8217;re\u00a0not, your point cloud will end up distorted.<\/span><\/p>\n<p><span data-contrast=\"auto\">A timing error of just a few milliseconds can cause a<\/span><span data-contrast=\"auto\">\u00a0<\/span><span data-contrast=\"auto\">survey\u00a0<\/span><span data-contrast=\"auto\">device to be metres away from its true position, stretching and shearing your final point cloud into a blurry, unusable mess. <\/span><span data-contrast=\"auto\">Imagine scanning a building as you drive past. If the timing is off, the front of the building will appear stretched or compressed, and straight lines will become warped and jagged.<\/span><\/p>\n<p><span data-contrast=\"auto\">Our <a href=\"https:\/\/networklessons.com\/ip-services\/precision-time-protocol-ptp-explained\" target=\"_blank\" rel=\"noopener\">PTP\/gPTP<\/a> synchronisation feature makes this easy. It uses a standard network protocol to make sure all your sensors are running on the same, super-accurate time, often with sub-microsecond precision.<\/span><\/p>\n<p><span data-contrast=\"auto\">It turns your entire sensor suite into a single, cohesive instrument operating on one unified timeline.\u00a0It&#8217;s\u00a0the critical link that ties the &#8220;where&#8221; (from the GNSS\/INS) to the &#8220;what&#8221; (from the LiDAR), and\u00a0it&#8217;s\u00a0an absolute must-have for any serious mobile mapping setup.<\/span><\/p>\n\n        <\/div>\n\n            <\/div>\n    <\/div>\n    \n            \n    <\/div>\n\n<div class=\"wp-block-image\">\n<figure class=\"alignleft size-large is-resized\"><img decoding=\"async\" width=\"1024\" height=\"345\" src=\"https:\/\/www.oxts.com\/wp-content\/uploads\/2025\/11\/OxTS-Car-Park-Pointcloud3-1024x345.png\" alt=\"Point cloud of a car park created using OXTS mobile mapping technology\" class=\"wp-image-10278\" style=\"width:697px;height:auto\" srcset=\"https:\/\/www.oxts.com\/wp-content\/uploads\/2025\/11\/OxTS-Car-Park-Pointcloud3-1024x345.png 1024w, https:\/\/www.oxts.com\/wp-content\/uploads\/2025\/11\/OxTS-Car-Park-Pointcloud3-300x101.png 300w, https:\/\/www.oxts.com\/wp-content\/uploads\/2025\/11\/OxTS-Car-Park-Pointcloud3-768x259.png 768w, https:\/\/www.oxts.com\/wp-content\/uploads\/2025\/11\/OxTS-Car-Park-Pointcloud3-1536x518.png 1536w, https:\/\/www.oxts.com\/wp-content\/uploads\/2025\/11\/OxTS-Car-Park-Pointcloud3-18x6.png 18w, https:\/\/www.oxts.com\/wp-content\/uploads\/2025\/11\/OxTS-Car-Park-Pointcloud3.png 1920w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\">OXTS Car Park Point Cloud<\/figcaption><\/figure>\n<\/div>\n\n\n<div class=\"conttitletext mwb-block py-4 lg:py-8\">\n        <div class=\"container md:grid md:grid-cols-12\">\n        <div class=\"col-span-6 col-start-5\">\n    \n           \n    \n            \n\n\n    \n\n\n            \n\n\n    \n\n\n\n        <div class=\"wysiwyg p\">\n            <h5 aria-level=\"2\"><span style=\"color: #ca181c;\">Stay accurate in the urban jungle with gx\/ix\u00a0<\/span><\/h5>\n<p><span data-contrast=\"auto\">A lot of\u00a0<\/span><span data-contrast=\"auto\">the most\u00a0<\/span><span data-contrast=\"auto\">valuable\u00a0mapping projects are in the heart of bustling cities, where tall buildings wreak havoc on GNSS signals, causing multipath errors and outright signal loss.<\/span><\/p>\n<p><span data-contrast=\"auto\">For a normal GNSS\/INS, this means a drifting, jumping trajectory with serious gaps occurring in the data. <\/span><span data-contrast=\"auto\">This could translate to warped buildings, misaligned road features, and a ruined point cloud that will either be rejected by the client or require a very costly re-survey.<\/span><\/p>\n<p><span data-contrast=\"auto\">Our gx\/ix technology is your best defence against this. It\u2019s an advanced proprietary algorithm that allows the GNSS\/INS to use raw data from fewer satellites than would normally be required for a standard position fix.\u00a0<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">When\u00a0operating\u00a0in ix mode, our system helps you by\u00a0maintaining\u00a0positioning accuracy when only one or two satellites are visible, using raw measurements to aid the inertial sensors and prevent drift.<\/span><span data-ccp-props=\"{&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Intelligent adaptation means that when you exit challenging environments like tunnels, the system returns to centimetre-level accuracy much faster than conventional systems, minimising data gaps and ensuring continuous measurement quality.<\/span><span data-ccp-props=\"{&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">With\u00a0gx\/ix,\u00a0you\u2019ll\u00a0keep your trajectory smooth and\u00a0accurate\u00a0where other systems fail, allowing you to take on challenging, and complex urban projects with true confidence.<\/span><\/p>\n<p><span style=\"color: #ca181c;\"><strong><a class=\"Hyperlink SCXW133846178 BCX0\" style=\"color: #ca181c;\" href=\"https:\/\/www.oxts.com\/solutions\/inertial-navigation-solutions\/tools-utilities-and-add-ons\/gx-ix-tight-coupling-processing\/\" target=\"_blank\" rel=\"noreferrer noopener\"><span class=\"TextRun Underlined SCXW133846178 BCX0\" lang=\"EN-GB\" xml:lang=\"EN-GB\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW133846178 BCX0\" data-ccp-charstyle=\"Hyperlink\">Learn more about\u00a0<\/span><span class=\"NormalTextRun SCXW133846178 BCX0\" data-ccp-charstyle=\"Hyperlink\">gx<\/span><span class=\"NormalTextRun SCXW133846178 BCX0\" data-ccp-charstyle=\"Hyperlink\">\/ix for georeferencing<\/span><\/span><\/a><\/strong><\/span><\/p>\n\n        <\/div>\n\n            <\/div>\n    <\/div>\n    \n            \n    <\/div>\n\n<div class=\"wp-block-image\">\n<figure class=\"alignleft size-large is-resized\"><img decoding=\"async\" width=\"1024\" height=\"507\" src=\"https:\/\/www.oxts.com\/wp-content\/uploads\/2026\/01\/Tunnel-1024x507.png\" alt=\"\" class=\"wp-image-10472\" style=\"aspect-ratio:1.7038961038961038;width:630px;height:auto\" srcset=\"https:\/\/www.oxts.com\/wp-content\/uploads\/2026\/01\/Tunnel-1024x507.png 1024w, https:\/\/www.oxts.com\/wp-content\/uploads\/2026\/01\/Tunnel-300x149.png 300w, https:\/\/www.oxts.com\/wp-content\/uploads\/2026\/01\/Tunnel-768x380.png 768w, https:\/\/www.oxts.com\/wp-content\/uploads\/2026\/01\/Tunnel-1536x761.png 1536w, https:\/\/www.oxts.com\/wp-content\/uploads\/2026\/01\/Tunnel-2048x1014.png 2048w, https:\/\/www.oxts.com\/wp-content\/uploads\/2026\/01\/Tunnel-18x9.png 18w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\">OXTS gx\/ix technology can help to ensure consistently accurate data in urban canyons<\/figcaption><\/figure>\n<\/div>\n\n\n<div class=\"conttitletext mwb-block py-4 lg:py-8\">\n        <div class=\"container md:grid md:grid-cols-12\">\n        <div class=\"col-span-6 col-start-5\">\n    \n           \n    \n            \n\n\n    \n\n\n            \n\n\n    \n\n\n\n        <div class=\"wysiwyg p\">\n            <h5 aria-level=\"3\"><span style=\"color: #ca181c;\">The\u00a0ultimate\u00a0safety\u00a0net:\u00a0raw\u00a0data\u00a0output\u00a0<\/span><\/h5>\n<p><span data-contrast=\"auto\">Large-scale survey jobs can last for hours and create massive amounts of data. You need a way to store it all and, crucially, the flexibility to squeeze every drop of accuracy out of it in post-processing.\u00a0<\/span><span data-ccp-props=\"{&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Our raw data output feature code is the answer. You can log all of your data to an external drive, so you&#8217;re not limited by internal storage. But more importantly,\u00a0 you can log the raw GNSS and IMU measurements.<\/span><\/p>\n<p><span data-contrast=\"auto\">This means you can take that raw data back to the office and use our powerful\u00a0NAVsolve\u00a0software to re-process it. You can apply more precise satellite correction data\u00a0if you have\u00a0it, or\u00a0run our\u00a0forwards\/backwards\u00a0processing algorithm to improve accuracy.<\/span><\/p>\n<p><span data-contrast=\"auto\">This combined &#8220;smoothing&#8221; process allows our Kalman filter to make more intelligent decisions, looking at the entire dataset to resolve ambiguities and create a final trajectory that&#8217;s even more accurate than what you could get in real-time.<\/span><\/p>\n<p><span data-contrast=\"auto\">It&#8217;s the ultimate flexibility when you need the best georeferencing results.<\/span><\/p>\n\n        <\/div>\n\n            <\/div>\n    <\/div>\n    \n            \n    <\/div>\n\n\n<div class=\"conttitletext mwb-block py-4 lg:py-8\">\n        <div class=\"container md:grid md:grid-cols-12\">\n        <div class=\"col-span-6 col-start-5\">\n    \n           \n    \n            \n\n\n    \n\n\n            \n\n\n    \n\n\n\n        <div class=\"wysiwyg p\">\n            <h5><span style=\"color: #ca181c;\">A simpler, faster workflow: building better point clouds without the headache with OXTS Georeferencer<\/span><\/h5>\n<p><span data-contrast=\"auto\">Combining GNSS\/INS data and raw LiDAR data has \u2013 until now \u2013 been time-consuming and complex. It involved clunky, command-line-driven third-party software, custom scripts, and a lot of manual data processing. This process was not only slow and required specialist knowledge, but it was also a common source of errors that could compromise the final deliverable.<\/span><\/p>\n<p><span data-contrast=\"auto\">OXTS Georeferencer makes that process simpler and faster, without compromising the quality of the output. Activating the feature code on your device enables you to export your data to our purpose-built tool with a simple, intuitive drag-and-drop interface. Give it the files, and it does the heavy lifting, creating a perfectly georeferenced point cloud in standard formats like .LAS.<\/span><\/p>\n<p><span data-contrast=\"auto\">It works with a huge range of LiDAR sensors from leaders in the industry &#8211;\u00a0<\/span><span data-contrast=\"auto\">Robosense<\/span><span data-contrast=\"auto\">, Ouster, Hesai, and more, streamlining your whole workflow.<\/span><\/p>\n<p><span data-contrast=\"auto\">This means less time spent on tedious data management, more time on value-added analysis, and dramatically reduced the training time for new team members, making your entire operation more flexible and scalable.<\/span><\/p>\n<p><span style=\"color: #ca181c;\"><a style=\"color: #ca181c;\" href=\"https:\/\/www.oxts.com\/solutions\/inertial-navigation-solutions\/software\/oxts-georeferencer\/\" target=\"_blank\" rel=\"noopener\"><b>Learn more about OXTS Georeferencer<\/b><\/a>\u00a0<\/span><\/p>\n\n        <\/div>\n\n            <\/div>\n    <\/div>\n    \n            \n    <\/div>\n\n\n<div class=\"conttitletext mwb-block py-4 lg:py-8\">\n        <div class=\"container md:grid md:grid-cols-12\">\n        <div class=\"col-span-6 col-start-5\">\n    \n           \n    \n            \n\n\n    \n\n\n            \n\n\n    \n\n\n\n        <div class=\"wysiwyg p\">\n            <h5 aria-level=\"3\"><span style=\"color: #ca181c;\">The\u00a0solution to\u00a0blurry point clouds: boresight calibration\u00a0<\/span><\/h5>\n<p><span data-contrast=\"auto\">Boresight misalignment is one of the hardest problems to solve\u00a0in mobile mapping.\u00a0It&#8217;s\u00a0the tiny, almost invisible difference in angle between the coordinate system of your INS and your LiDAR.\u00a0<\/span><span data-ccp-props=\"{&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">A heading error of just 0.2 degrees can throw off a point by 7 cm at\u00a0a distance of only\u00a010 meters.\u00a0<\/span><span data-ccp-props=\"{&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">This shows up as a blurry &#8220;double vision&#8221; effect in your\u00a0final point\u00a0cloud. Flat surfaces like walls will look fuzzy and thick, and sharp corners will appear rounded and indistinct, making the data useless for precise measurements and\u00a0damaging\u00a0the quality of the point cloud.<\/span><\/p>\n<p><span data-contrast=\"auto\">The boresight calibration tool in OXTS\u00a0Georeferencer\u00a0is the ultimate solution.\u00a0<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Instead of trying to measure these angles by hand with a ruler and protractor (which is impossible to do accurately), you perform a quick, 2-3 minute calibration drive around our boresight targets or a flat, feature-rich wall, making sure to capture it from multiple angles. Our software then analyses how these static features appear in the data and automatically calculates the exact angular offsets with a precision you could never get manually. Doing this quick step at the start of your survey saves you hours of frustrating cleanup work later and guarantees a sharp, accurate point cloud &#8211; it&#8217;s all about getting it right from the start.<\/span><\/p>\n<p><span data-contrast=\"auto\">The whole process is a simple, three-step workflow right within the OXTS ecosystem:<\/span><span data-ccp-props=\"{&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<ol>\n<li><b><span data-contrast=\"auto\">Process your trajectory:<\/span><\/b><span data-contrast=\"auto\">\u00a0Run your raw INS data through\u00a0NAVsolve. This is where you apply the best correction data and use forward\/backward processing to create the most accurate possible path for your vehicle.<\/span><span data-ccp-props=\"{&quot;335559738&quot;:240}\">\u00a0<\/span><\/li>\n<li><b><span data-contrast=\"auto\">Align your sensors:<\/span><\/b><span data-contrast=\"auto\">\u00a0Use the simple, data-driven Boresight tool in OXTS Georeferencer to precisely calculate and eliminate the angular alignment errors between your sensors.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<li><b><span data-contrast=\"auto\">Georeference:<\/span><\/b><span data-contrast=\"auto\">\u00a0Use\u00a0OXTS\u00a0Georeferencer to combine your perfect trajectory and calibrated LiDAR data. This final step applies the timing, position, orientation, and boresight data to every single LiDAR point, creating a final, crisp, and accurate point cloud.<\/span><\/li>\n<\/ol>\n\n        <\/div>\n\n            <\/div>\n    <\/div>\n    \n            \n    <\/div>\n\n\n<div class=\"conttitletext mwb-block py-4 lg:py-8\">\n        <div class=\"container md:grid md:grid-cols-12\">\n        <div class=\"col-span-6 col-start-5\">\n    \n           \n    \n            \n\n\n    \n\n\n            \n\n\n    \n\n\n\n        <div class=\"wysiwyg p\">\n            <h5><span style=\"color: #ca181c;\">From data capture to competitive edge<\/span><\/h5>\n<p><span data-contrast=\"auto\">An OXTS system is more than just a GNSS\/INS. It&#8217;s a complete, integrated ecosystem engineered to address the modern surveyor&#8217;s dual georeferencing needs for accuracy and efficiency.<\/span><\/p>\n<p><span data-contrast=\"auto\">It represents a fundamental shift in the mobile mapping workflow: from a reactive process of cleaning up errors in post-processing to a proactive approach of getting the data right from the very start.<\/span><\/p>\n<p><span data-contrast=\"auto\">This &#8220;get it right&#8221; philosophy is your competitive advantage. Our software and hardware features guarantee data integrity in the most challenging field conditions, while the software tools dramatically accelerate the path to your final deliverable.<\/span><\/p>\n<p><span data-contrast=\"auto\">Talk to our applications engineers today to map the right feature codes and start getting cleaner, faster, more repeatable georeferencing results.<\/span><\/p>\n<p><span data-contrast=\"auto\"><a href=\"https:\/\/www.oxts.com\/contact\/\">Contact us<\/a> to learn more about how you can maximise the value of your existing OXTS hardware.<\/span><\/p>\n\n        <\/div>\n\n            <\/div>\n    <\/div>\n    \n            \n    <\/div>\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n\n<div class=\"hero001center mwb-block bg-left bg-cover bg-repeat-x lazyload relative\"     \n\n\n\n    style=\"background-image: url('https:\/\/www.oxts.com\/wp-content\/uploads\/2025\/12\/Artboard-1-100x0-c-default.png')\"\n    data-bg=\"https:\/\/www.oxts.com\/wp-content\/uploads\/2025\/12\/Artboard-1-1400x0-c-default.png\"\n>\n    <div class=\"overlay bg-black\/30 absolute inset-0 z-0\"><\/div>\n    <div class=\"container text-white flex justify-left\">\n        <div class=\"max-w-lg pb-64 pt-16 md:pb-36 md:pt-36 relative z-10\">\n            \n                \n\n\n    \n\n\n                \n\n\n    \n\n    <h3 class=\"h3 inline-block max-w-xl mb-6 \">Download the RT3000 v4 Datasheet<\/h3>\n\n                \n\n\n    \n\n    <p class=\"p inline-block  max-w-lg mb-6 \">Learn more about the specifications you can expect from our flagship inertial navigation system the RT3000 v4.<\/p>\n\n                \n\n\n\n\n    <div class=\"justify-left btns\">\n                        \n\n\n        \n    <a href=\"https:\/\/www.oxts.com\/the-rt3000-v4-datasheet\/\" class=\"btn primary outlined\" target=\"_blank\">\n                    <span>Download the RT3000 v4 Datasheet<\/span>\n        <\/a>\n\n            <\/div>\n\n        <\/div>\n    <\/div>\n    <div id=\"info-block_4377f849a86c205dae038efe960a8a5c\"><\/div>\n<\/div>\n\n\n<div class=\"conttitletext mwb-block py-4 lg:py-8\">\n        <div class=\"container md:grid md:grid-cols-12\">\n        <div class=\"col-span-6 col-start-5\">\n    \n           \n    \n            \n\n\n    \n\n\n            \n\n\n    \n\n\n\n        <div class=\"wysiwyg p\">\n            \n        <\/div>\n\n            <\/div>\n    <\/div>\n    \n            \n    <\/div>","protected":false},"excerpt":{"rendered":"","protected":false},"author":5,"featured_media":10481,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[81,92,106,50],"tags":[82,70,75,64,65,85,76],"class_list":["post-10458","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-mobile-mapping","category-oxts","category-software","category-georeferencing","tag-georeferencing","tag-gnss","tag-gnss-denied-localisation","tag-imu","tag-ins","tag-rt3000-v4","tag-wayfinder"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.oxts.com\/zh\/wp-json\/wp\/v2\/posts\/10458","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.oxts.com\/zh\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.oxts.com\/zh\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.oxts.com\/zh\/wp-json\/wp\/v2\/users\/5"}],"replies":[{"embeddable":true,"href":"https:\/\/www.oxts.com\/zh\/wp-json\/wp\/v2\/comments?post=10458"}],"version-history":[{"count":16,"href":"https:\/\/www.oxts.com\/zh\/wp-json\/wp\/v2\/posts\/10458\/revisions"}],"predecessor-version":[{"id":11386,"href":"https:\/\/www.oxts.com\/zh\/wp-json\/wp\/v2\/posts\/10458\/revisions\/11386"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.oxts.com\/zh\/wp-json\/wp\/v2\/media\/10481"}],"wp:attachment":[{"href":"https:\/\/www.oxts.com\/zh\/wp-json\/wp\/v2\/media?parent=10458"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.oxts.com\/zh\/wp-json\/wp\/v2\/categories?post=10458"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.oxts.com\/zh\/wp-json\/wp\/v2\/tags?post=10458"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}