This research describes an obstacle detection algorithm, using feature-point tracking, for Unmanned Aerial Vehicles (UAV) in cluttered environments, suitable for applications where non-visual mapping techniques may perform poorly. Monocular sensors are particularly attractive hardware selections for this application due to their relative simplicity, economy of scale, and cost. Although many visual mapping techniques currently exist, limited focus has been directed towards the use of a rolling-shutter camera, due to its progressive scan nature of image acquisition and subsequently coupled time-delay. Because a rolling-shutter camera captures a frame by progressively scanning pixels in a sequential manner, the time a feature-point is encountered is dependent on its location in the image frame. This introduces a time-delay between frame time-stamp and actual feature-point encounter. Presented here is a means of compensating for this time-delay, improving mapping performance. Unlike the limited library of past work on this matter, where the vehicle’s trajectory is approximated by a piecewise polynomial function with a sparse number of control points, this work parameterises a camera frame’s line scan time, then draws on a buffer of the vehicle’s past states to interpolate to our approximated time-point. This approximated state is then used to sequentially update our database of landmark position estimates. The contribution of this current work is to present a recursive map estimation algorithm which compensates for this time-delay using an EKF framework, with landmark inverse depth parameterisation. The estimator performances is then directly compared, with and without time-delay compensation, in Monte Carlo simulation.