Using shape information from natural tree landmarks for improving SLAM performance
Localization and mapping are crucial components for robotic autonomy. However, such robots must often function in remote, outdoor areas with no a-priori knowledge of the environment. Consequently, it becomes necessary for field robots to be able to construct their own maps based on exteroceptive sensor readings. To this end, visual sensing and mapping through naturally occurring landmarks have distinct advantages. With the availability of high bandwidth data provided by visual sensors, meaningful and uniquely identifiable objects can be detected. This improves the construction of maps consisting of natural landmarks that are meaningful for human readers as well. In this thesis, we focus on the use of trees in an outdoor environment as a suitable set of landmarks for Simultaneous Localization and Mapping (SLAM). Trees have a relatively simple, near vertical structure which makes them easily and consistently detectable. Furthermore, the thickness of a tree can be accurately determined from different viewpoints. Our primary contribution is the usage of the width of a tree trunk as an additional sensory reading, allowing us to include the radius of tree trunks on the map. To this end, we introduce a new sensor model that relates the width of a tree landmark on the image plane to the radius of its trunk. We provide a mathematical formulation of this model, derive associated Jacobians and incorporate our sensor model into a working EKF SLAM implementation. Through simulations we show that the use of this new sensory reading improves the accuracy of both the map and the trajectory estimates without additional sensor hardware other than a monocular camera.