Sensing Technology Survey for Obstacle Detection in Vegetation

Shreya Lohar, Lei Zhu, Stanley Young, Peter Graf, Michael Blanton

Research output: Contribution to journalArticlepeer-review

7 Scopus Citations


This study reviews obstacle detection technologies in vegetation for autonomous vehicles or robots. Autonomous vehicles used in agriculture and as lawn mowers face many environmental obstacles that are difficult to recognize for the vehicle sensor. This review provides information on choosing appropriate sensors to detect obstacles through vegetation, based on experiments carried out in different agricultural fields. The experimental setup from the literature consists of sensors placed in front of obstacles, including a thermal camera; red, green, blue (RGB) camera; 360° camera; light detection and ranging (LiDAR); and radar. These sensors were used either in combination or single-handedly on agricultural vehicles to detect objects hidden inside the agricultural field. The thermal camera successfully detected hidden objects, such as barrels, human mannequins, and humans, as did LiDAR in one experiment. The RGB camera and stereo camera were less efficient at detecting hidden objects compared with protruding objects. Radar detects hidden objects easily but lacks resolution. Hyperspectral sensing systems can identify and classify objects, but they consume a lot of storage. To obtain clearer and more robust data of hidden objects in vegetation and extreme weather conditions, further experiments should be performed for various climatic conditions combining active and passive sensors.
Original languageAmerican English
Pages (from-to)672-685
Number of pages14
JournalFuture Transportation
Issue number3
StatePublished - 2021

NREL Publication Number

  • NREL/JA-5400-81950


  • autonomous vehicle spatial sensing
  • data fusion
  • lidar
  • obstacle detection in vegetation
  • radar
  • thermal camera


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