Localizing Perception
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Localizing Perception

While pinpointing the position of a Smart Machine and its surroundings seems like a solved issue, the reality is that it is still a challenge when the objective is to allow a mass-produced, affordable and robust solution.

  • GPS/GNSS's precision is in the range of meters and often unreliable for autonomous operation purposes.
    For example, Urban Canyons are still a problem for Satellite-based localization.


    However, they provide an absolute position on Earth, which is very useful for high-level mapping and other user-interface purposes.

    Interestingly, there is significant innovation in this field, including technologies that have the potential to provide much precise location data like PPP-IAR (Precise Point Positioning with Integer Ambiguities Resolution).

  • IMUs (Inertial Measurement Units) are helpful and much more precise than GPS for short periods of time before drift becomes too significant.


    Also in this field there are significant improvements in precision at lower costs as new uses cases emerge, but this can't change its fundamental working principles.
  • Wheel encoders provide some sense of movement and measurement of speed, but they can't be trusted alone because of slipping wheels, changing diameter and other factors.
    Wheel encoder
    They provide a robust 0 velocity information ("I'm sure that I'm not moving") that IMUs are unable to provide in a reliable or cost-effective way.

For best results, you can combine these different sensors and methods to get a more robust localization. These are often integrated with a previously-created map, which also creates the new problem of being able to update them.

While we believe these approaches are useful and should be used together, Outsight's 3D Semantic Camera deliver an uncorrelated Localization output (Relative Ego-motion if there is no reference map, Absolute Localization if there is a map) thanks to a SLAM-on-Chip(R) algorithm (Simultaneous Localization and Mapping embedded on a Chip) that relies only on Perception data.


Localization summary-1


This 3D SLAM capability was originally developed by the company Dibotics, now part of Outsight, and is a major contribution to improve robustness and availability of Localization data for Smart Machines and therefore to Outsight mission of bringing Full Situation Awareness to Smart Machines.