LiDAR Data Processing with Python: Turn 3D Laser Scanner Point Clouds into Detailed Spatial Maps for Autonomous Mobile Robots

Name: LiDAR Data Processing with Python: Turn 3D Laser Scanner Point Clouds into Detailed Spatial Maps for Autonomous Mobile Robots
Brand: Independently Published
SKU: 9798189694639
Price: 1615.0 INR
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ISBN13: 9798189694639
Binding: Paperback
Subject: N/A
Publisher: Independently Published
Publisher Imprint: Independently Published
Publication Date: July 29, 2026
Pages: 188
Original Price: GBP 14.29
Language: English
Edition: N/A
Item Weight: 236 grams
BISAC Subject(s): Artificial Intelligence / Computer Vision & Pattern Recognition

About The Book

Turn raw 3D laser scans into clean, useful maps that autonomous mobile robots can understand.

A 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.

LiDAR Data Processing with Python 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.

This beginner-friendly guide will help you:

Understand how LiDAR sensors measure distance and produce point clouds
Work with X, Y, and Z coordinates, intensity, returns, timestamps, classifications, and colour attributes
Prepare a clean Python workspace for point-cloud processing
Load, inspect, and convert PCD, PLY, LAS, LAZ, XYZ, CSV, and NumPy point-cloud files
Read large LiDAR files safely without exhausting memory
Visualise point clouds from multiple viewpoints using Open3D and Matplotlib
Remove invalid coordinates, distant points, robot-body points, noise, and outliers
Use voxel, uniform, random, statistical, radius, and intensity-based filtering methods
Work with coordinate frames, translation, rotation, transformation matrices, and sensor-to-robot conversions
Detect ground surfaces, slopes, ramps, and height above ground
Segment floors, walls, ceilings, pallets, boxes, and other objects
Group points with DBSCAN and measure objects with bounding boxes
Align and combine multiple scans using registration and ICP concepts
Create two-dimensional occupancy grid maps for robot navigation
Build elevation and traversability maps that identify slopes, rough ground, curbs, steps, and unsafe terrain
Process recorded and live robot LiDAR data safely
Connect processed clouds and maps to ROS 2 navigation workflows
Improve performance with chunking, tiling, reusable functions, configurable pipelines, and batch jobs
Build a complete robot-ready mapping pipeline as the final project

The 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.

No 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.

The 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.

Build cleaner point-cloud workflows, create better spatial maps, and prepare LiDAR data for safer autonomous mobile robot navigation.

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