Range Sensing

  • Kurt KonoligeEmail author
  • Andreas Nüchter
Part of the Springer Handbooks book series (SHB)


Range sensors are devices that capture the three-dimensional (3-D ) structure of the world from the viewpoint of the sensor, usually measuring the depth to the nearest surfaces. These measurements could be at a single point, across a scanning plane, or a full image with depth measurements at every point. The benefits of this range data is that a robot can be relatively certain where the real world is, relative to the sensor, thus allowing the robot to more reliably find navigable routes, avoid obstacles, grasp objects, act on industrial parts, etc.

This chapter introduces the main representations for range data (point sets, triangulated surfaces, voxels), the main methods for extracting usable features from the range data (planes, lines, triangulated surfaces), the main sensors for acquiring it (Sect. 31.1 – stereo and laser triangulation and ranging systems), how multiple observations of the scene, for example, as if from a moving robot, can be registered (Sect. 31.3) and several indoor and outdoor robot applications where range data greatly simplifies the task (Sect. 31.4).


Point Cloud Range Image Query Point Range Sensor Robotic Application 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.











application-specific feature transform


Defense Advanced Research Projects Agency


degree of freedom


digital signal processor


expectation maximization


frequency modulation continuous wave


field of view


field-programmable gate array


global positioning system


graphics processing unit


iterative closest point


inertial measurement unit




laser radar


light-emitting diode


light detection and ranging


laser measurement system


Laplacian of Gaussian


multilevel surface map


principal component analysis


personal computer


point feature histogram


random sample consensus


structure from motion


scale-invariant feature transform


simultaneous localization and mapping


signal-to-noise ratio


singular value decomposition




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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Google, Inc.Mountain ViewUSA
  2. 2.Informatics VII – Robotics and TelematicsUniversity of WürzburgWürzburgGermany

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