Stable Laser Interest Point Selection for Place Recognition in a Forest

Place recognition is an essential part of robot localization and mapping problems. Using lower data-rate sensors like 2D scanning laser rangefinders enables the robots to use less memory and computation in building maps. However, place recognition by a vehicle with 6-DOF dynamics like a quadrotor in unstructured, 3D environments like forests is challenging, especially with a sensor that only measures a planar slice of the environment. This paper extends the 2D geometry-based place recognition system of [1] to a challenging forest envirnoment with a novel procedure for selecting stable and salient 2D laser interest points using Dirichlet process clustering (DP-means). This method is tested on both synthetic and real data from a forest trail and compared with [1]. The result reveals the importance of salient interest point selection in allowing accurate and fast place recognition. Our approach also ensures a low bandwidth representation of visited areas, making it suitable for real-time, multi-agent SLAM applications.