access icon free Correlation scan matching algorithm based on multi-resolution auxiliary historical point cloud and lidar simultaneous localisation and mapping positioning application

The matching algorithm is an important part of simultaneous location and mapping. Aiming at the problem of large computation and poor real-time performance of two-dimensional lidar traditional correlation scan matching (CSM) algorithm, a multi-resolution auxiliary historical point cloud matching algorithm is proposed, which combines high and low resolution and adopts a single-frame to multi-frame step-by-step matching scheme. The algorithm was carried out on the sweeping robot. Compared with the traditional CSM algorithm and iterative closest points algorithm, the single position accuracy of the method in this study is improved. In the indoor space of ∼10 m × 10 m, the cumulative error is reduced by 16.24 and 33.96%, respectively. Consequently, our algorithm can still manage to process in real-time.

Inspec keywords: position control; mobile robots; optical radar; iterative methods; SLAM (robots); image matching

Other keywords: multiframe step-by-step matching scheme; high resolution; simultaneous location; size 10.0 m; lidar simultaneous localisation; mapping positioning application; two-dimensional lidar traditional correlation scan matching algorithm; single position accuracy; multiresolution auxiliary historical point cloud matching algorithm; iterative closest points algorithm; traditional CSM algorithm

Subjects: Optical radar; Spatial variables control; Mobile robots; Optical, image and video signal processing; Image recognition; Computer vision and image processing techniques

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