Rodrigues, RT ; Tsiogkas, N; Aguiar, AP; Pascoal, AM
IROS 2020, Las Vegas, NV, USA, October 24, 2020 – January 24, 2021
ID Authenticus: P-00T-GV7
DOI: 10.1109/iros45743.2020.9341768
Abstract: In this paper, we propose a mapping technique that builds a continuous representation of the environment from range data. The strategy presented here encodes the probability of points in space to be occupied using 2.5D B-spline surfaces. For a fast update rate, the surface is recursively updated as new measurements arrive. The proposed B-spline map is less susceptible to precision and interpolation errors that are present in occupancy grid-based methods. From simulation and experimental results, we show that this approach leverages the floating point resolution of continuous metric maps and the fast update/access/merging advantages of discrete metric maps. Thus, the proposed method is suitable for online robotic tasks such as localization and path planning, requiring minor modification to existing software that usually operates on metric maps. © 2020 IEEE.