{"product_id":"resepi-gen-ii-m2x-ilx-used","title":"RESEPI Gen-II M2X-ILX - Used","description":"\u003cp\u003eThe RESEPI GEN-II system is the latest addition to the RESEPI family of products and is the result of years of market research and disciplined practice of the fundamentals of engineering. This surveyor’s dream is built for an unrivaled ability to be versatile in nature; serving and delivering the best in class data for a variety of use cases spanning aerial, mobile, and pedestrian based applications. Feature rich and intuitive workflows allow for confident and successful operation regardless of the size of the project at hand.\u003c\/p\u003e\n\u003ch3\u003eLiDAR\u003c\/h3\u003e\n\u003cp\u003eRESEPI GEN-II with XT-32M2X LiDAR makes use of the enhanced version of the mobile XT-32 LiDAR scanner which features a lighter weight and extended detection range over its predecessor model. It enables an M2X- equipped RESEPI to fly longer, combined with a higher recommended maximum AGL and wider vertical FOV to more effectively map larger areas and sites with dense vegetation cover. It is an all-around desirable system, offering the benefits of best-in-class data accuracy, better detection range, high point density, and versatility.\u003c\/p\u003e\n\u003ch3\u003eOverview\u003c\/h3\u003e\n\u003cp\u003eThe RESEPI™ (Remote Sensing Payload Instrument) GEN-II payload is an advanced, next-generation, sensor-fusion platform designed for accuracy-focused real-time and post-processed aerial, mobile, and pedestrian-based remote sensing applications. At its core, RESEPI GEN-II utilizes the Inertial Labs’ Dual Antenna Inertial Navigation System (INS-D), a high-performance and expandable navigation system powered by Inertial Labs’ Extended Kalman Filter (EKF). Within this system lies a Tactical Grade Inertial Measurement Unit (IMU), the Kernel-210, also by Inertial Labs. RESEPI GEN-II provides more expandability over its predecessor by offering the ability for end-users and integrators to have tight integrations with their platforms by taking advantage of built-in software integrations made for and by MAVLink and DJI’s Payload SDK (PSDK). Benefit from two new camera options with a wider field of view, faster shutter speeds, and higher resolution images. Field-swappable mounts and accessories open up easy integrations with well-known platforms such as the WISPR Ranger Pro 1100, Freefly Astro, Sony Airpeak S1, and DJI M350. In addition, the RESEPI GEN-II platform features a significantly more powerful on-board computing module that opens up the ability for real-time point cloud visualization and further integrations with external\/additional sensing modules, giving users the ability to integrate and synchronize their additional cameras and LiDAR’s; or input aiding data to the navigation filter from wheel speed sensors, encoders, external IMU’s or Air Data Computers (ADC). This complete payload is perfectly suited for plug-and-play with end-users and engineering firms looking to adopt a hardware package that offers customization and expandability with a versatile remote sensing solution.\u003c\/p\u003e\n\u003ch3\u003eApplications\u003c\/h3\u003e\n\u003cp\u003eThe RESEPI GEN-II XT-32M2X was strategically designed for multiple application bases with mounting options for mobile vehicles, DJI supported drones (DJI M300, M350, M600 Pro), custom drones, handheld platforms (including the RESEPI LiDAR Payload Backpack), indoor robotics vehicles, the Freefly Alta-X and Astro, WISPR Ranger Pro 1100, Sony Airpeak S1, and many more. Because of this diverse mounting portfolio, the RESEPI can be used for many services including utilities mapping (power lines), construction volumetrics, site surveying, precision agriculture, forestry, mining operations, and much more.\u003c\/p\u003e\n\u003ch3\u003eSystem Specification\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003eSystem Vertical Accuracy 2-3 cm\u003c\/li\u003e\n\u003cli\u003ePrecision 2-4 cm\u003c\/li\u003e\n\u003cli\u003ePrecision (1σ Noise Removal) 1.5-2.5 cm\u003c\/li\u003e\n\u003cli\u003eRecommended AGL 150 meters\u003c\/li\u003e\n\u003cli\u003eWeight 1.7 kg\u003c\/li\u003e\n\u003cli\u003eDimensions 21.6 X 17.8 X 13 cm\u003c\/li\u003e\n\u003cli\u003eMax Flight Time (DJI M300) 33 minutes\u003c\/li\u003e\n\u003cli\u003eInternal Storage, SSD 512 GB\u003c\/li\u003e\n\u003cli\u003eSystem Computer Hexacore 8 GB DDR4 RAM, 16 GB eMMC\u003c\/li\u003e\n\u003cli\u003eOperational Voltage Range 9-50 volt\u003c\/li\u003e\n\u003cli\u003ePower Consumption 25 watt\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch3\u003eLiDAR Scanner\u003c\/h3\u003e\n\u003ctable style=\"width: 100.012%;\" width=\"100%\" data-mce-style=\"width: 100.012%;\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 49.1244%;\" data-mce-style=\"width: 49.1244%;\"\u003eLaser Range Capabilities\u003cbr\u003e\n\u003c\/td\u003e\n\u003ctd style=\"width: 50.4417%;\" data-mce-style=\"width: 50.4417%;\"\u003e80m @ 10% ref. (all channels) 0.05 to 300m \u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 49.1244%;\" data-mce-style=\"width: 49.1244%;\"\u003eRange Accuracy\u003cbr\u003e\n\u003c\/td\u003e\n\u003ctd style=\"width: 50.4417%;\" data-mce-style=\"width: 50.4417%;\"\u003e+\/- 1 cm\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 49.1244%;\" data-mce-style=\"width: 49.1244%;\"\u003eFOV (Horizontal)\u003cbr\u003e\n\u003c\/td\u003e\n\u003ctd style=\"width: 50.4417%;\" data-mce-style=\"width: 50.4417%;\"\u003e360°\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 49.1244%;\" data-mce-style=\"width: 49.1244%;\"\u003eFOV (Vertical)\u003cbr\u003e\n\u003c\/td\u003e\n\u003ctd style=\"width: 50.4417%;\" data-mce-style=\"width: 50.4417%;\"\u003e40.3°\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 49.1244%;\" data-mce-style=\"width: 49.1244%;\"\u003eScan Angle (Vertical)\u003cbr\u003e\n\u003c\/td\u003e\n\u003ctd style=\"width: 50.4417%;\" data-mce-style=\"width: 50.4417%;\"\u003e-20.8° to 19.5° \u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 49.1244%;\" data-mce-style=\"width: 49.1244%;\"\u003eBeam Divergence\u003cbr\u003e\n\u003c\/td\u003e\n\u003ctd style=\"width: 50.4417%;\" data-mce-style=\"width: 50.4417%;\"\u003e0.056° (H), 0.1° (V) \u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 49.1244%;\" data-mce-style=\"width: 49.1244%;\"\u003eLaser Channels\u003cbr\u003e\n\u003c\/td\u003e\n\u003ctd style=\"width: 50.4417%;\" data-mce-style=\"width: 50.4417%;\"\u003e32\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 49.1244%;\" data-mce-style=\"width: 49.1244%;\"\u003eNumber of Returns\u003cbr\u003e\n\u003c\/td\u003e\n\u003ctd style=\"width: 50.4417%;\" data-mce-style=\"width: 50.4417%;\"\u003e3\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 49.1244%;\" data-mce-style=\"width: 49.1244%;\"\u003ePulse Rate\u003cbr\u003e\n\u003c\/td\u003e\n\u003ctd style=\"width: 50.4417%;\" data-mce-style=\"width: 50.4417%;\"\u003e\n\u003cp\u003e640 k\/s (single return) \u003c\/p\u003e\n\u003cp\u003e1,280 k\/s (dual return)\u003cbr\u003e\u003c\/p\u003e\n\u003cp\u003e1,920 k\/s (triple return)\u003c\/p\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003ch3\u003eCamera\u003c\/h3\u003e\n\u003ctable width=\"100%\" style=\"width: 100.012%; height: 98px;\" data-mce-style=\"width: 100.012%; height: 98px;\"\u003e\n\u003ctbody\u003e\n\u003ctr style=\"height: 19.6px;\" data-mce-style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 49.3359%; height: 19.6px;\" data-mce-style=\"width: 49.3359%; height: 19.6px;\"\u003e Model\u003c\/td\u003e\n\u003ctd style=\"width: 50.2302%; height: 19.6px;\" data-mce-style=\"width: 50.2302%; height: 19.6px;\"\u003eSony ILX-LR1\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\" data-mce-style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 49.3359%; height: 19.6px;\" data-mce-style=\"width: 49.3359%; height: 19.6px;\"\u003eResolution\u003cbr\u003e\n\u003c\/td\u003e\n\u003ctd style=\"width: 50.2302%; height: 19.6px;\" data-mce-style=\"width: 50.2302%; height: 19.6px;\"\u003e61MP\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\" data-mce-style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 49.3359%; height: 19.6px;\" data-mce-style=\"width: 49.3359%; height: 19.6px;\"\u003eLens\u003cbr\u003e\n\u003c\/td\u003e\n\u003ctd style=\"width: 50.2302%; height: 19.6px;\" data-mce-style=\"width: 50.2302%; height: 19.6px;\"\u003eFixed, Manual Focus, 18mm\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\" data-mce-style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 49.3359%; height: 19.6px;\" data-mce-style=\"width: 49.3359%; height: 19.6px;\"\u003eMax Trigger Rate\u003cbr\u003e\n\u003c\/td\u003e\n\u003ctd style=\"width: 50.2302%; height: 19.6px;\" data-mce-style=\"width: 50.2302%; height: 19.6px;\"\u003e1 seconds\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\" data-mce-style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 49.3359%; height: 19.6px;\" data-mce-style=\"width: 49.3359%; height: 19.6px;\"\u003eField of View\u003cbr\u003e\n\u003c\/td\u003e\n\u003ctd style=\"width: 50.2302%; height: 19.6px;\" data-mce-style=\"width: 50.2302%; height: 19.6px;\"\u003e100°\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 49.3359%;\" data-mce-style=\"width: 49.3359%;\"\u003eEstimated GSD Accuracy\u003cbr\u003e\n\u003c\/td\u003e\n\u003ctd style=\"width: 50.2302%;\" data-mce-style=\"width: 50.2302%;\"\u003e2 cm at 50 m AGL\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003ch3\u003eGPS-Aided INS\u003c\/h3\u003e\n\u003ctable width=\"100%\" style=\"width: 100.012%;\" data-mce-style=\"width: 100.012%;\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 49.3209%;\" data-mce-style=\"width: 49.3209%;\"\u003eIMU\u003cbr\u003e\n\u003c\/td\u003e\n\u003ctd style=\"width: 50.2452%;\" data-mce-style=\"width: 50.2452%;\"\u003eInertial Labs Kernel-210 \u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 49.3209%;\" data-mce-style=\"width: 49.3209%;\"\u003eGNSS\u003cbr\u003e\n\u003c\/td\u003e\n\u003ctd style=\"width: 50.2452%;\" data-mce-style=\"width: 50.2452%;\"\u003eNovAtel OEM7720\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 49.3209%;\" data-mce-style=\"width: 49.3209%;\"\u003eConstellations\u003cbr\u003e\n\u003c\/td\u003e\n\u003ctd style=\"width: 50.2452%;\" data-mce-style=\"width: 50.2452%;\"\u003eGPS, GLONASS, Galileo, BeiDou, QZSS, NavIC (IRNSS), SBAS, L-Band \u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 49.3209%;\" data-mce-style=\"width: 49.3209%;\"\u003eFrequencies\u003cbr\u003e\n\u003c\/td\u003e\n\u003ctd style=\"width: 50.2452%;\" data-mce-style=\"width: 50.2452%;\"\u003eL1, L2, L5\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 49.3209%;\" data-mce-style=\"width: 49.3209%;\"\u003eOperation Modes\u003cbr\u003e\n\u003c\/td\u003e\n\u003ctd style=\"width: 50.2452%;\" data-mce-style=\"width: 50.2452%;\"\u003eRTK and PPK\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 49.3209%;\" data-mce-style=\"width: 49.3209%;\"\u003eINS Algorithm\u003cbr\u003e\n\u003c\/td\u003e\n\u003ctd style=\"width: 50.2452%;\" data-mce-style=\"width: 50.2452%;\"\u003eExtended Kalman Filter\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 49.3209%;\" data-mce-style=\"width: 49.3209%;\"\u003eOutput Rates\u003cbr\u003e\n\u003c\/td\u003e\n\u003ctd style=\"width: 50.2452%;\" data-mce-style=\"width: 50.2452%;\"\u003eUp to 200 HZ (INS) , Up to 2,000 HZ (IMU)\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 49.3209%;\" data-mce-style=\"width: 49.3209%;\"\u003ePitch\/Roll Accuracy\u003cbr\u003e\n\u003c\/td\u003e\n\u003ctd style=\"width: 50.2452%;\" data-mce-style=\"width: 50.2452%;\"\u003e0.03 (RTK) , 0.006 (PPK)\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 49.3209%;\" data-mce-style=\"width: 49.3209%;\"\u003eHeading Accuracy\u003cbr\u003e\n\u003c\/td\u003e\n\u003ctd style=\"width: 50.2452%;\" data-mce-style=\"width: 50.2452%;\"\u003e0.08 (RTK) , 0.03 (PPK)\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 49.3209%;\" data-mce-style=\"width: 49.3209%;\"\u003eVelocity Accuracy\u003cbr\u003e\n\u003c\/td\u003e\n\u003ctd style=\"width: 50.2452%;\" data-mce-style=\"width: 50.2452%;\"\u003e\u0026lt;0.03 m\/s\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 49.3209%;\" data-mce-style=\"width: 49.3209%;\"\u003ePosition Accuracy\u003cbr\u003e\n\u003c\/td\u003e\n\u003ctd style=\"width: 50.2452%;\" data-mce-style=\"width: 50.2452%;\"\u003e1 cm + 1 ppm (RTK) , 0.5 cm (PPK)\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003ch3\u003eIntegration Support\u003c\/h3\u003e\n\u003ctable style=\"width: 100.012%;\" width=\"100%\" data-mce-style=\"width: 100.012%;\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 49.3355%;\" data-mce-style=\"width: 49.3355%;\"\u003eExternal Camera Support \u003c\/td\u003e\n\u003ctd style=\"width: 50.2305%;\" data-mce-style=\"width: 50.2305%;\"\u003eYes\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 49.3355%;\" data-mce-style=\"width: 49.3355%;\"\u003eExternal LiDAR Support\u003c\/td\u003e\n\u003ctd style=\"width: 50.2305%;\" data-mce-style=\"width: 50.2305%;\"\u003eYes\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 49.3355%;\" data-mce-style=\"width: 49.3355%;\"\u003eExternal GNSS Receiver Support\u003c\/td\u003e\n\u003ctd style=\"width: 50.2305%;\" data-mce-style=\"width: 50.2305%;\"\u003eYes\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 49.3355%;\" data-mce-style=\"width: 49.3355%;\"\u003eOdometer or Wheel Speed Sensor Support\u003c\/td\u003e\n\u003ctd style=\"width: 50.2305%;\" data-mce-style=\"width: 50.2305%;\"\u003eYes\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 49.3355%;\" data-mce-style=\"width: 49.3355%;\"\u003eMAVLink and DJI Payload SDK Capable \u003c\/td\u003e\n\u003ctd style=\"width: 50.2305%;\" data-mce-style=\"width: 50.2305%;\"\u003eYes\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003ch3\u003eSoftware (PCMasterPro)\u003c\/h3\u003e\n\u003ctable width=\"100%\" style=\"width: 100.012%;\" data-mce-style=\"width: 100.012%;\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 49.3355%;\" data-mce-style=\"width: 49.3355%;\"\u003eField Checks\u003c\/td\u003e\n\u003ctd style=\"width: 50.2305%;\" data-mce-style=\"width: 50.2305%;\"\u003eYes\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 49.3355%;\" data-mce-style=\"width: 49.3355%;\"\u003ePre-Processing\u003c\/td\u003e\n\u003ctd style=\"width: 50.2305%;\" data-mce-style=\"width: 50.2305%;\"\u003eYes\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 49.3355%;\" data-mce-style=\"width: 49.3355%;\"\u003ePost-Processing\u003c\/td\u003e\n\u003ctd style=\"width: 50.2305%;\" data-mce-style=\"width: 50.2305%;\"\u003eYes, Supported \u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 49.3355%;\" data-mce-style=\"width: 49.3355%;\"\u003eSLAM (Powered by Kudan)\u003c\/td\u003e\n\u003ctd style=\"width: 50.2305%;\" data-mce-style=\"width: 50.2305%;\"\u003eYes\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 49.3355%;\" data-mce-style=\"width: 49.3355%;\"\u003eStrip Alignment (Powered by BayesMap) \u003c\/td\u003e\n\u003ctd style=\"width: 50.2305%;\" data-mce-style=\"width: 50.2305%;\"\u003eYes\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 49.3355%;\" data-mce-style=\"width: 49.3355%;\"\u003eAdditional Features\u003c\/td\u003e\n\u003ctd style=\"width: 50.2305%;\" data-mce-style=\"width: 50.2305%;\"\u003eCoordinate System Transformation, Batch Processing, Open Architecture, Noise Filtering, etc. \u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e","brand":"Inertial Labs","offers":[{"title":"Default Title","offer_id":49495423123683,"sku":"PRD250423-001-USED","price":33675.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0400\/1752\/6943\/files\/RESEPI_GEN-II_M2X-ILX.png?v=1728056131","url":"https:\/\/e38surveysolutions.com\/products\/resepi-gen-ii-m2x-ilx-used","provider":"E38 Survey Solutions","version":"1.0","type":"link"}