https://beta-website.mainstreetautonomy.com/ Recent content on Main Street Autonomy Hugo en-us https://beta-website.mainstreetautonomy.com/case-studies/overland-ai/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/case-studies/overland-ai/ Overland AI runs Calibration Anywhere daily across a growing fleet of Ultra autonomous tactical ground vehicles, replacing a manual target-based process. https://beta-website.mainstreetautonomy.com/case-studies/auve-tech/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/case-studies/auve-tech/ Auve Tech runs Calibration Anywhere on a fleet of buses, ensuring sensors are well calibrated for safe autonomous operation in a wide range of operating environments. https://beta-website.mainstreetautonomy.com/docs/working-with-msa/getting-started/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/docs/working-with-msa/getting-started/ How to start working with Main Street Autonomy https://beta-website.mainstreetautonomy.com/docs/working-with-msa/system-requirements/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/docs/working-with-msa/system-requirements/ System, sensor, and data requirements for working with Main Street Autonomy https://beta-website.mainstreetautonomy.com/docs/working-with-msa/capturing-data/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/docs/working-with-msa/capturing-data/ How to capture sensor data to work with Main Street Autonomy https://beta-website.mainstreetautonomy.com/docs/working-with-msa/data-portal/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/docs/working-with-msa/data-portal/ How to use the Data Portal to upload sensor data and download results from Main Street Autonomy https://beta-website.mainstreetautonomy.com/docs/working-with-msa/glossary/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/docs/working-with-msa/glossary/ Definition of terms used by Main Street Autonomy https://beta-website.mainstreetautonomy.com/docs/working-with-msa/faq/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/docs/working-with-msa/faq/ Frequently asked questions about working with Main Street Autonomy https://beta-website.mainstreetautonomy.com/docs/calibration-anywhere/how-it-works/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/docs/calibration-anywhere/how-it-works/ How Calibration Anywhere performs automatic sensor calibration to deliver extrinsics, intrinsics, and time offsets for all sensors https://beta-website.mainstreetautonomy.com/docs/calibration-anywhere/calibration-motion/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/docs/calibration-anywhere/calibration-motion/ How to move your system while capturing data for Calibration Anywhere automatic sensor calibration https://beta-website.mainstreetautonomy.com/docs/calibration-anywhere/calibration-results/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/docs/calibration-anywhere/calibration-results/ The extrinsic, intrinsic, and time offsets calibration outputs from Calibration Anywhere https://beta-website.mainstreetautonomy.com/docs/calibration-anywhere/calibration-validation/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/docs/calibration-anywhere/calibration-validation/ How to validate Calibration Anywhere results https://beta-website.mainstreetautonomy.com/docs/calibration-anywhere/deployment/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/docs/calibration-anywhere/deployment/ How to deploy Calibration Anywhere automatic sensor calibration software https://beta-website.mainstreetautonomy.com/docs/calibration-anywhere/targetless-calibration/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/docs/calibration-anywhere/targetless-calibration/ How targetless calibration works https://beta-website.mainstreetautonomy.com/docs/calibration-anywhere/extrinsics-and-intrinsics/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/docs/calibration-anywhere/extrinsics-and-intrinsics/ Extrinsic and intrinsic sensor calibration explained https://beta-website.mainstreetautonomy.com/docs/calibration-anywhere/time-offset-calibration/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/docs/calibration-anywhere/time-offset-calibration/ Time offset calibration explained https://beta-website.mainstreetautonomy.com/docs/calibration-anywhere/faq/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/docs/calibration-anywhere/faq/ Frequently asked questions about Calibration Anywhere automatic sensor calibration https://beta-website.mainstreetautonomy.com/docs/pose-engine/how-it-works/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/docs/pose-engine/how-it-works/ How Pose Engine generates 6DoF pose and odometry from sensor data, offline and online https://beta-website.mainstreetautonomy.com/docs/pose-engine/pose-results/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/docs/pose-engine/pose-results/ The pose trajectory and odometry outputs from Pose Engine, offline and online https://beta-website.mainstreetautonomy.com/docs/pose-engine/deployment/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/docs/pose-engine/deployment/ How to deploy Pose Engine as a remote service, on-prem Docker container, or online library https://beta-website.mainstreetautonomy.com/docs/pose-engine/mapping-and-localization/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/docs/pose-engine/mapping-and-localization/ How Pose Engine builds maps and localizes robots within them https://beta-website.mainstreetautonomy.com/docs/pose-engine/coordinate-frames/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/docs/pose-engine/coordinate-frames/ The coordinate frames used in Pose Engine offline and online outputs https://beta-website.mainstreetautonomy.com/docs/pose-engine/bootstrapping/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/docs/pose-engine/bootstrapping/ How Pose Engine determines the robot's initial pose when starting a new online localization session https://beta-website.mainstreetautonomy.com/docs/pose-engine/faq/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/docs/pose-engine/faq/ Frequently asked questions about Pose Engine perception-based localization https://beta-website.mainstreetautonomy.com/docs/lidar/what-lidar-does/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/docs/lidar/what-lidar-does/ What lidar measures and how distance and bearing combine into 3D point clouds https://beta-website.mainstreetautonomy.com/docs/lidar/measuring-distance/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/docs/lidar/measuring-distance/ How lidar measures distance — pulsed time-of-flight, amplitude modulated, FMCW, and parallax ranging https://beta-website.mainstreetautonomy.com/docs/lidar/discerning-bearing/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/docs/lidar/discerning-bearing/ How lidar determines the direction of returns — arrays, scanning, and beam steering methods https://beta-website.mainstreetautonomy.com/docs/lidar/wavelength-and-eye-safety/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/docs/lidar/wavelength-and-eye-safety/ Near-IR vs 1550 nm lidar wavelengths, eye safety regulations, and their practical implications https://beta-website.mainstreetautonomy.com/docs/lidar/laser-sources/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/docs/lidar/laser-sources/ The three common laser types used in automotive lidar — VCSELs, edge-emitting diodes, and fiber lasers https://beta-website.mainstreetautonomy.com/docs/lidar/common-lidar-problems/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/docs/lidar/common-lidar-problems/ Beam angle calibration, range offsets, blooming, intensity-dependent bias, encoder hysteresis, and multi-lidar alignment https://beta-website.mainstreetautonomy.com/docs/lidar/faq/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/docs/lidar/faq/ Frequently asked questions about lidar — terminology, solid state, and common misconceptions https://beta-website.mainstreetautonomy.com/docs/camera/what-cameras-do/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/docs/camera/what-cameras-do/ What a camera pixel measures, the pinhole camera model, and how cameras are used in robotics and autonomy. https://beta-website.mainstreetautonomy.com/docs/camera/image-sensors-and-shutter/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/docs/camera/image-sensors-and-shutter/ CMOS image sensor technology for robotics, and the difference between rolling and global shutter. https://beta-website.mainstreetautonomy.com/docs/camera/exposure-and-dynamic-range/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/docs/camera/exposure-and-dynamic-range/ Exposure time, gain, motion blur, auto-exposure, and high dynamic range for cameras on moving robots. https://beta-website.mainstreetautonomy.com/docs/camera/lenses-and-distortion/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/docs/camera/lenses-and-distortion/ Focal length, field of view, aperture, lens distortion models, and intrinsic camera calibration. https://beta-website.mainstreetautonomy.com/docs/camera/color-and-image-pipeline/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/docs/camera/color-and-image-pipeline/ Bayer color filter arrays, demosaicing, the ISP pipeline, color spaces, and why robotics often uses monochrome sensors. https://beta-website.mainstreetautonomy.com/docs/camera/output-formats-and-compression/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/docs/camera/output-formats-and-compression/ Resolution, frame rate, pixel formats, and video compression as they affect bandwidth and latency. https://beta-website.mainstreetautonomy.com/docs/camera/depth/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/docs/camera/depth/ Recovering depth from cameras with passive stereo, structured light, and time-of-flight, plus camera synchronization. https://beta-website.mainstreetautonomy.com/docs/camera/common-camera-problems/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/docs/camera/common-camera-problems/ The image and depth artifacts encountered when running cameras on robots, and their causes. https://beta-website.mainstreetautonomy.com/docs/camera/faq/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/docs/camera/faq/ Frequently asked questions about cameras for robotics and autonomy. https://beta-website.mainstreetautonomy.com/about/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/about/ Main Street Autonomy builds production software for autonomous systems. https://beta-website.mainstreetautonomy.com/resources/autonomous-lawnmower-operating-with-perception-based-localization/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/resources/autonomous-lawnmower-operating-with-perception-based-localization/ MSA co-developed a fully autonomous lawnmower shown operating with perception-based localization. Lidar, camera, IMU, and wheel encoder data are used to build a map of the perimeter of the mow site. The mower then fills in the mow site with rows driven to cm-level accuracy, leaving no "mow-hawks" of uncut grass. Calibration Anywhere is used to calibrate sensors, and Pose Engine is used for localization. https://beta-website.mainstreetautonomy.com/calibration/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/calibration/ Extrinsics, intrinsics, and time offsets for every sensor in under 10 minutes. Works in any environment. No targets. No checkerboards. https://beta-website.mainstreetautonomy.com/resources/calibration-anywhere-software-introduction/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/resources/calibration-anywhere-software-introduction/ Main Street Autonomy's Calibration Anywhere software calibrates perception sensors, like lidars, radars, cameras, IMUs, and GPS units, using only sensor data captured during motion. The process is easy! Move the robot near some static structure, store the sensor data, and run the calibration. In maybe 10 minutes, you'll get extrinsics: 6-dof pose for all sensors (lidars, radars, cameras, IMUs, and GPS units), plus a base_link kinematic frame reference. Plus intrinsics: openCV-compatible lens models for cameras (including depth), rolling shutter times, wheel encoder gain, and lidar and depth intrinsics. Plus time offsets for all sensors relative to each other. The calibration is repeatible, reliable, and can happen anywhere. You don't need checkerboards, targets, or engineers involved in the calibration process. The benefits are significant: improve your sensor data quality, enable straightforward sensor fusion, and unblock your perception team. Don't spend your time wrestling with calibration, get MSA to take care of it for you. https://beta-website.mainstreetautonomy.com/resources/camera-localization-indy-autonomous-racecar-texas-motor-speedway/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/resources/camera-localization-indy-autonomous-racecar-texas-motor-speedway/ This is the MIT-PITT-RW Indy Autonomous Challenge racecar at Texas Motor Speedway with Pose Engine visual localization to a pre-built map. We're using all sensors for the localization, including the cameras, lidars, IMUs, wheel encoders, and differential GPS. https://beta-website.mainstreetautonomy.com/resources/colorized-lidar-figure-8-calibration-motion-parking-lot/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/resources/colorized-lidar-figure-8-calibration-motion-parking-lot/ A colorized lidar sensor fusion visualization (where each point is a lidar range measurement painted with the associated pixel color) shows how the operator of this robot rapidly executes the figure-8 calibration motion for Calibration Anywhere. The visualization demonstrates perfect fusion even during high-speed turns and fast motion. Blue pixels are outside the shared field-of-view of the lidar and camera. https://beta-website.mainstreetautonomy.com/resources/colorized-lidar-figure-8-calibration-motion-offroad/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/resources/colorized-lidar-figure-8-calibration-motion-offroad/ A colorized lidar sensor fusion visualization (where each point is a lidar range measurement painted with the associated pixel color) shows how the operator of this robot rapidly executes the figure-8 calibration motion for Calibration Anywhere. The visualization demonstrates perfect fusion even during bumpy offroad operation. Blue pixels are outside the shared field-of-view of the lidar and camera. https://beta-website.mainstreetautonomy.com/resources/feature-map-and-track-figure-8-calibration-motion-warehouse/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/resources/feature-map-and-track-figure-8-calibration-motion-warehouse/ A colorized lidar sensor fusion visualization (where each point is a lidar range measurement painted with the associated pixel color) shows how the operator of this robot rapidly executes the figure-8 calibration motion for Calibration Anywhere. The visualization demonstrates perfect fusion even during bumpy offroad operation. Blue pixels are outside the shared field-of-view of the lidar and camera. https://beta-website.mainstreetautonomy.com/resources/feature-map-and-track-figure-8-calibration-motion-nvidia-carter-warehouse/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/resources/feature-map-and-track-figure-8-calibration-motion-nvidia-carter-warehouse/ A colorized lidar sensor fusion visualization (where each point is a lidar range measurement painted with the associated pixel color) shows how the nVIDIA Carter robot moves in a figure-8 calibration motion for Calibration Anywhere. The visualization demonstrates perfect sensor fusion in the indoor warehouse environment. https://beta-website.mainstreetautonomy.com/resources/ihmc-nadia-humanoid-robot-playing-ping-pong-calibrated-by-msa/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/resources/ihmc-nadia-humanoid-robot-playing-ping-pong-calibrated-by-msa/ Nadia is an advanced, highly mobile humanoid robot developed by the Florida Institute for Human and Machine Cognition (IHMC) in collaboration with Boardwalk Robotics. Main Street Autonomy provided critical camera calibrations using Calibration Anywhere software. https://beta-website.mainstreetautonomy.com/resources/lidar-camera-sensor-fusion-on-road/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/resources/lidar-camera-sensor-fusion-on-road/ Lidar and camera sensor fusion: colorized lidar on the left, where each point is a lidar range measurement, painted the appropriate pixel color from the camera images. The right shows lidar-in-camera, where lidar range measurements are projected into the camera images. Note the cameras used on this vehicle have wide fisheye lenses. Additionally, all four cameras are rolling shutter. https://beta-website.mainstreetautonomy.com/resources/lidar-localization-and-lidar-scan-map-of-lawrenceville-pa/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/resources/lidar-localization-and-lidar-scan-map-of-lawrenceville-pa/ This visualization shows a lidar scan map built from a robot moving on the sidewalk in Lawrenceville, PA. The robot has a lidar, IMU, and wheel encoders. The breadcrumbs show the robot trajectory, determined by MSA Pose Engine perception-based localization software. Lidar scans captured from the robot are coregistered to the robot's pose, motion compensated, and shown in a world frame. The quality and coherence of the resulting lidar scan map is due to the accurate sensor calibration and accurate localization of the robot. Calibration or localization inaccuracies would appear in the fused lidar clouds. https://beta-website.mainstreetautonomy.com/resources/lidar-scan-map-parking-garage/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/resources/lidar-scan-map-parking-garage/ This visualization shows a lidar scan map built from a vehicle traversing the Bakery Square Parking Garage in Pittsburgh, PA. The sensor system is comprised of an Ouster lidar and an IMU mounted on the roof of a car. The green breadcrumbs show the vehicle trajectory, determined by MSA Pose Engine perception-based localization software. Lidar scans captured from the vehicle are coregistered to the vehicle pose, motion compensated, and shown in a world frame. The quality and coherence of the resulting lidar scan map is due to the accurate sensor calibration and accurate localization of the vehicle. Calibration or localization inaccuracies would appear in the fused lidar clouds. https://beta-website.mainstreetautonomy.com/resources/lidar-in-camera-sensor-fusion-on-road/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/resources/lidar-in-camera-sensor-fusion-on-road/ Lidar-in-camera sensor fusion, where lidar range measurements are projected into the camera images. Note this camera is rolling shutter with a wide fisheye lens and is generating images at 30Hz. The lidar is an Ouster OS0 capturing data at 10Hz. https://beta-website.mainstreetautonomy.com/resources/multi-lidar-multi-camera-sensor-fusion-offroad/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/resources/multi-lidar-multi-camera-sensor-fusion-offroad/ Main Street Autonomy can simplify the challenges your robotics team is facing. MSA's Calibration Anywhere software generated the 6dof pose, camera lens and lidar beam intrinsics, and time offsets for the 3D lidars, four RGB rolling-shutter cameras, wheel encoders, and GPS antenna. With an excellent calibration, sensor fusion, perception, mapping, and localization are easier. https://beta-website.mainstreetautonomy.com/resources/multi-robot-outdoor-lidar-localization/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/resources/multi-robot-outdoor-lidar-localization/ One robot maps the Allegheny Cemetery (blue/green), a second maps the nearby Lawrenceville PA area (orange/purple). The robots are both using Ouster lidars; neither is using GPS/GNSS. The visualization demonstrates causal localization. Map merging is shown after the maps are built. https://beta-website.mainstreetautonomy.com/resources/msa-sensor-calibration-nvidia-perceptor-workflow/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/resources/msa-sensor-calibration-nvidia-perceptor-workflow/ Workflow for calibrating sensors for nVIDIA Isaac Perceptor. https://beta-website.mainstreetautonomy.com/localization/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/localization/ Offline and online 6DoF pose and odometry from lidars and/or cameras. GNSS-independent. Accurate, precise, and deterministic. https://beta-website.mainstreetautonomy.com/privacy/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/privacy/ Privacy policy for Main Street Autonomy, LLC. https://beta-website.mainstreetautonomy.com/resources/sensor-extrinsics-for-marble-robot/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/resources/sensor-extrinsics-for-marble-robot/ Sensor extrinsics are illustrated on a robot wireframe: two lidars, four cameras, IMU, and GNSS. The robot kinematic frame is shown between the two drive wheels. This robot was built by Marble Robot, Inc (acquired by Caterpillar in 2020). https://beta-website.mainstreetautonomy.com/resources/sensor-extrinsics-for-nvidia-carter-robot/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/resources/sensor-extrinsics-for-nvidia-carter-robot/ Sensor extrinsics are illustrated on a robot wireframe: two lidars, four cameras, IMU, and GNSS. The robot kinematic frame is shown between the two drive wheels. This robot was built by Marble Robot, Inc (acquired by Caterpillar in 2020). https://beta-website.mainstreetautonomy.com/resources/sensor-fusion-figure-8-calibration-motion-warehouse/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/resources/sensor-fusion-figure-8-calibration-motion-warehouse/ A colorized lidar sensor fusion visualization (where each point is a lidar range measurement painted with the associated pixel color) shows the figure-8 calibration motion of this robot in a warehouse. https://beta-website.mainstreetautonomy.com/resources/sensor-fusion-figure-8-calibration-motion-nvidia-carter-warehouse/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/resources/sensor-fusion-figure-8-calibration-motion-nvidia-carter-warehouse/ A colorized lidar sensor fusion visualization (where each point is a lidar range measurement painted with the associated pixel color) shows the how the nVIDIA Carter robot moves in a figure-8 calibration motion for Calibration Anywhere. https://beta-website.mainstreetautonomy.com/resources/sensor-fusion-visualization-outside-inside-warehouse/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/resources/sensor-fusion-visualization-outside-inside-warehouse/ The visualization shows colorized lidar: each point shown is a lidar range measurement that is painted with the color of the appropriate pixel captured by the cameras. The lidar and cameras are not time synchronized and are capturing data at different rates (10Hz and 30Hz, respectively). To generate this output, the lidar and camera sensors must be perfectly calibrated for the extrinsic pose of the sensors, the intrinsic corrections for the camera lens, and the time offsets between the lidar and cameras. https://beta-website.mainstreetautonomy.com/demo/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/demo/ Share sensor data from your robot. We return real results. No cost, no obligation. https://beta-website.mainstreetautonomy.com/demo/thanks/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/demo/thanks/ Thanks for reaching out to Main Street Autonomy. We review every submission and will be in touch. https://beta-website.mainstreetautonomy.com/resources/visual-localization-and-mapbuilding-outside-inside-warehouse/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/resources/visual-localization-and-mapbuilding-outside-inside-warehouse/ The visualization shows the various tracked feature points and map of the vehicle's trajectory. Pose Engine visual localization is generating the pose estimates shown here, using camera images fused with IMU and wheel encoders. https://beta-website.mainstreetautonomy.com/resources/visual-localization-multistory-parking-garage/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/resources/visual-localization-multistory-parking-garage/ This visualization follows the path of an autonomous vehicle building a map of the Bakery Square Parking Garage in Pittsburgh, PA. The localization shown here uses the output from four cameras and an IMU mounted on the roof of a car. No GPS/GNSS data are used. Pose Engine is shown generating 6DoF pose estimates. The localization algorithm consumes 3-4 cores of an Nvidia Jetson processor. https://beta-website.mainstreetautonomy.com/resources/visual-localization-multirotor-drone-cameras-only/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/resources/visual-localization-multirotor-drone-cameras-only/ Data capture from a small quadcopter drone with two GoPro cameras attached. No IMU, no lidar, no GPS/GNSS, and no other sensors were used. The video shows how we use image features for both visual odometry, to build a map of the environment, and to localize within the map. You can see the blue crosses in the video identifying various features; cross size scales with image patch size. The map view on the bottom shows keyframes being created and connected in a covisibility graph, along with the point cloud of the local sparse map currently being tracked in the imagery. The keyframes in the local map have their little camera pairs drawn above them. https://beta-website.mainstreetautonomy.com/resources/visual-localization-farm-tractor/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/resources/visual-localization-farm-tractor/ This robot is a hybrid autonomous/human piloted farm tractor with four cameras and an IMU. The robot has no lidar and no GPS/GNSS. The video shows how we use image features for visual odometry, to build a map of the environment, and to localize within the map. You can see the blue crosses in the video identifying various features; cross size scales with image patch size. Note how the four camera feeds have independently-controlled white balance and lots of image artifacts: dirt on the lenses, glare and reflections on all four cameras, and a bouncing seat in the FOV. Our software handles these without delaying the pose estimate latency or requiring massive compute. The map view on the bottom shows keyframes being created and connected in a covisibility graph, along with the point cloud of the local sparse map currently being tracked in the images. The keyframes in the local map have little camera icons indicating the pose of the sensors at that moment. https://beta-website.mainstreetautonomy.com/resources/what-is-a-camera-calibration/ Mon, 01 Jan 0001 00:00:00 +0000 https://beta-website.mainstreetautonomy.com/resources/what-is-a-camera-calibration/ Cameras map points in the world into pixels, calibration tells us how the mapping works. Pixel coordinates are used to find a ray in 3D space, which can be used to measure the world.