Optimization of the simultaneous localization and map-building algorithm for real-time implementation

TLDR

Real‑time implementation of the simultaneous localization and map‑building (SLAM) algorithm is the focus of the study. The study proposes optimal algorithms that exploit matrix structure and a compressed filler to drastically reduce computation for local areas or high‑frequency sensors. The authors develop algorithms that use the special form of matrices, a compressed filler, and near‑optimal simplifications with a suitable map representation. Experiments show that extending Kalman filter models keeps local area information at O(N_a^2) cost, transfers it to the full map in a single iteration, and performs well on a vehicle in an unstructured outdoor environment.

Abstract

Addresses real-time implementation of the simultaneous localization and map-building (SLAM) algorithm. It presents optimal algorithms that consider the special form of the matrices and a new compressed filler that can significantly reduce the computation requirements when working in local areas or with high frequency external sensors. It is shown that by extending the standard Kalman filter models the information gained in a local area can be maintained with a cost /spl sim/O(N/sub a//sup 2/), where N/sub a/ is the number of landmarks in the local area, and then transferred to the overall map in only one iteration at full SLAM computational cost. Additional simplifications are also presented that are very close to optimal when an appropriate map representation is used. Finally the algorithms are validated with experimental results obtained with a standard vehicle running in a completely unstructured outdoor environment.