Data describing aircraft position and attitude are essential to computing return positions from ranging data collected during airborne laser scanning (ALS) campaigns. However, these data are often excluded from the products delivered to the client and their recovery after the contract is complete can require negotiations with the data provider, may involve additional costs, or even be infeasible. This paper presents a rigorous, fully automated, novel method for recovering aircraft positions using only the point cloud. The study used ALS data from five acquisitions in the US Pacific Northwest region states of Oregon and Washington and validated derived aircraft positions using the smoothed best estimate of trajectory (SBET) provided for the acquisitions. The computational requirements of the method are reduced and precision is improved by relying on subsets of multiple-return pulses, common in forested areas, with widely separated first and last returns positioned at opposite sides of the aircraft to calculate their intersection, or closest point of approach. To provide a continuous trajectory, a cubic spline is fit to the intersection points. While it varies by acquisition and parameter settings, the error in the computed aircraft position seldom exceeded a few meters. This level of error is acceptable for most applications. To facilitate use and encourage modifications to the algorithm, the authors provide a code that can be applied to data from most ALS acquisitions.
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