2015 · 11 citations · 28 references
ComputingComputer ScienceRobotics EngineeringComputer VisionRobot PerceptionRoboticsLandmark-based LocalizationMappingObstacle DetectionPose EstimationRobot LocalizationRobotic Vision3D Obstacle DetectionContainmentContainment DetectionLandmark KnowledgeOdometryRobot MappingRobotic LocalizationVision-based Localization
In this paper, we present an omnidirectional-vision-based localization and mapping system which can detect whether a robotic mower is contained in a permitted area. We exploit a robot-centric mapping framework that exploits a differential equation of motion of the landmarks, which are referenced with respect to the robot body frame. The estimator in our system generates a 3D point-based map with landmarks. Concurrently, the estimator defines a boundary of the mowing area with the estimated trajectory of the mower. The estimated boundary and the landmark map are provided for the estimation of the mowing location and for the containment detection. We validate the effectiveness of our system through numerical simulations and present the results of the outdoor experiment that we conducted with our robotic mower.
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SVO: Fast semi-direct monocular visual odometry
Christian Förster, Matia Pizzoli, Davide Scaramuzza · 2014 · 2.1K citations
Maximum likelihood estimates of linear dynamic systems
Herbert E. Rauch, F. Tung, Charlotte Striebel · AIAA Journal · 1965 · 1.9K citations