Estimating uncertain spatial relationships in robotics
Autonomous robot vehicles
FastSLAM: a factored solution to the simultaneous localization and mapping problem
Eighteenth national conference on Artificial intelligence
Alice: An information-rich autonomous vehicle for high-speed desert navigation: Field Reports
Journal of Robotic Systems - Special Issue on the DARPA Grand Challenge, Part 2
Odin: Team VictorTango's entry in the DARPA Urban Challenge
Journal of Field Robotics - Special Issue on the 2007 DARPA Urban Challenge, Part I
Team Cornell's Skynet: Robust perception and planning in an urban environment
Journal of Field Robotics - Special Issue on the 2007 DARPA Urban Challenge, Part I
Junior: The Stanford entry in the Urban Challenge
Journal of Field Robotics - Special Issue on the 2007 DARPA Urban Challenge, Part II
A perception-driven autonomous urban vehicle
Journal of Field Robotics - Special Issue on the 2007 DARPA Urban Challenge, Part III
Finding multiple lanes in urban road networks with vision and lidar
Autonomous Robots
Lane boundary and curb estimation with lateral uncertainties
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
A High-rate, Heterogeneous Data Set From The DARPA Urban Challenge
International Journal of Robotics Research
RSLAM: A System for Large-Scale Mapping in Constant-Time Using Stereo
International Journal of Computer Vision
Ford Campus vision and lidar data set
International Journal of Robotics Research
Progress toward multi-robot reconnaissance and the MAGIC 2010 competition
Journal of Field Robotics
Navigation toward non-static target object using footprint detection based tracking
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
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Recent applications of robotics often demand two types of spatial awareness: 1) A fine-grained description of the robot's immediate surroundings for obstacle avoidance and planning, and 2) Knowledge of the robot's position in a large-scale global coordinate frame such as that provided by GPS. Although managing information at both of these scales is often essential to the robot's purpose, each scale has different requirements in terms of state representation and handling of uncertainty. In such a scenario, it can be tempting to pick either a body-centric coordinate frame or a globally fixed coordinate frame for all state representation. Although both choices have advantages, we show that neither is ideal for a system that must handle both global and local data. This paper describes an alternative design: a third coordinate frame that stays fixed to the local environment over short time-scales, but can vary with respect to the global frame. Careful management of uncertainty in this local coordinate frame makes it well-suited for simultaneously representing both locally and globally derived data, greatly simplifying system design and improving robustness. We describe the implementation of this coordinate frame and its properties when measuring uncertainty, and show the results of applying this approach to our 2007 DARPA Urban Challenge vehicle.