Lidar-Based Semantic Mapping of Indoor Environments

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Light detection and ranging (or lidar) sensors have revolutionized the nascent field of autonomous driving. The ability of lidar to generate accurate point cloud maps of the surrounding environment is essential to maintain situational awareness. Although lidar has primarily been deployed for self-driving vehicles to date, there are many other potential application areas. In particular, lidar can play an essential role in large-scale indoor mapping - the rich lidar data stream can be used to identify and track both known and novel objects indoors. Semantic maps, which contain information about individual object instances, are useful for inventory management in warehouses, for example. The Alliance research program will develop breakthrough capabilities in the area of lidar-based semantic mapping for warehousing and intralogistics applications. Intralogistics (which combines warehouse automation and logistics) is a $15 billion industry today and is expected reach $27 billion in size globally by 2025. In partnership with LeddarTech, Inc., the proposed Alliance research program will leverage machine learning to extract unique material and object signatures from lidar data streams in real time, improving the accuracy of semantic maps in cluttered indoor scenes. Enhanced semantic mapping capabilities will enable LeddarTech to enter new markets and to grow its offerings for existing markets, including the AV sector.