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A considerably difficult aspect of Simultaneous Localization and Mapping (SLAM) is the problem of uncertainty constrained long term point-to-point navigation where global loop closures to eliminate estimation biases may not be possible. In such scenarios, a prime concern is to control the rate of localization error growth. We have developed fundamental results on the underlying problem of localization and a novel SLAM technique that allows sub-meter localization error for a > 100 km trajectory without loop closure and using only on-board sensors, i.e., no GPS.
Applications may include:
- Self-driving cars
- Precision farming
- Planetary rovers
- Unmanned Aerial Vehicles
- Autonomous Underwater Vehicles
To license this technology for commercial use or to learn more, please contact lab director Dr. Suman Chakravorty or Dr. Ismail Sheikh (smismail[at]tamu[dot]edu) at Texas A&M University Technology Commercialization, 800 Raymond Stotzer Parkway, Suite 2020, College Station, Texas 77845.