{"product_id":"lidar-data-processing-with-python-turn-3d-laser-scanner-point-clouds-into-detailed-spatial-maps-for-autonomous-mobile-robots-9798189694639","title":"LiDAR Data Processing with Python: Turn 3D Laser Scanner Point Clouds into Detailed Spatial Maps for Autonomous Mobile Robots","description":"\u003cp\u003e • Author(s): Nathan Westwood\u003cbr\u003e • Publisher: Independently Published\u003cbr\u003e • Publisher Imprint: Independently Published\u003cbr\u003e • BISAC: Computer Vision \u0026amp; Pattern Recognition\u003c\/p\u003e\u003cp\u003e\u003cb\u003eTurn raw 3D laser scans into clean, useful maps that autonomous mobile robots can understand.\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eA LiDAR sensor can capture thousands or millions of distance measurements, but a cloud of points is not automatically a robot-ready map. Raw scans must be inspected, cleaned, transformed, aligned, segmented, and converted into spatial information that navigation software can use.\u003c\/p\u003e\u003cp\u003e\u003ci\u003eLiDAR Data Processing with Python\u003c\/i\u003e teaches that complete workflow step by step. Using Python, NumPy, Open3D, laspy, SciPy, Matplotlib, scikit-learn, optional PDAL tools, and ROS 2 concepts, you will learn how to turn point clouds into practical occupancy, elevation, and traversability maps for autonomous mobile robots.\u003c\/p\u003e\u003cp\u003eThis beginner-friendly guide will help you: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eUnderstand how LiDAR sensors measure distance and produce point clouds\u003c\/li\u003e\n\u003cli\u003eWork with X, Y, and Z coordinates, intensity, returns, timestamps, classifications, and colour attributes\u003c\/li\u003e\n\u003cli\u003ePrepare a clean Python workspace for point-cloud processing\u003c\/li\u003e\n\u003cli\u003eLoad, inspect, and convert PCD, PLY, LAS, LAZ, XYZ, CSV, and NumPy point-cloud files\u003c\/li\u003e\n\u003cli\u003eRead large LiDAR files safely without exhausting memory\u003c\/li\u003e\n\u003cli\u003eVisualise point clouds from multiple viewpoints using Open3D and Matplotlib\u003c\/li\u003e\n\u003cli\u003eRemove invalid coordinates, distant points, robot-body points, noise, and outliers\u003c\/li\u003e\n\u003cli\u003eUse voxel, uniform, random, statistical, radius, and intensity-based filtering methods\u003c\/li\u003e\n\u003cli\u003eWork with coordinate frames, translation, rotation, transformation matrices, and sensor-to-robot conversions\u003c\/li\u003e\n\u003cli\u003eDetect ground surfaces, slopes, ramps, and height above ground\u003c\/li\u003e\n\u003cli\u003eSegment floors, walls, ceilings, pallets, boxes, and other objects\u003c\/li\u003e\n\u003cli\u003eGroup points with DBSCAN and measure objects with bounding boxes\u003c\/li\u003e\n\u003cli\u003eAlign and combine multiple scans using registration and ICP concepts\u003c\/li\u003e\n\u003cli\u003eCreate two-dimensional occupancy grid maps for robot navigation\u003c\/li\u003e\n\u003cli\u003eBuild elevation and traversability maps that identify slopes, rough ground, curbs, steps, and unsafe terrain\u003c\/li\u003e\n\u003cli\u003eProcess recorded and live robot LiDAR data safely\u003c\/li\u003e\n\u003cli\u003eConnect processed clouds and maps to ROS 2 navigation workflows\u003c\/li\u003e\n\u003cli\u003eImprove performance with chunking, tiling, reusable functions, configurable pipelines, and batch jobs\u003c\/li\u003e\n\u003cli\u003eBuild a complete robot-ready mapping pipeline as the final project\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eThe book builds one practical workflow rather than disconnected demonstrations. You will begin by inspecting individual point-cloud files, then progress through cleaning, transformation, ground detection, segmentation, registration, occupancy mapping, elevation mapping, traversability analysis, ROS 2 publishing, and complete pipeline organisation.\u003c\/p\u003e\u003cp\u003eNo LiDAR sensor is required to begin. Sample files support the core exercises, while live-data and robot-navigation sections can be explored later with suitable hardware and ROS 2 software.\u003c\/p\u003e\u003cp\u003eThe focus is practical robot mapping. You will learn why coordinate frames, units, thresholds, timestamps, map resolution, obstacle expansion, and validation matter before a robot is allowed to trust the resulting map.\u003c\/p\u003e\u003cb\u003eBuild cleaner point-cloud workflows, create better spatial maps, and prepare LiDAR data for safer autonomous mobile robot navigation.\u003c\/b\u003e","brand":"Independently Published","offers":[{"title":"Paperback","offer_id":48214360195223,"sku":"9798189694639","price":1615.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9798189694639.webp?v=1788729566","url":"https:\/\/atlanticbooks.com\/products\/lidar-data-processing-with-python-turn-3d-laser-scanner-point-clouds-into-detailed-spatial-maps-for-autonomous-mobile-robots-9798189694639","provider":"Atlantic Books","version":"1.0","type":"link"}