Natural corners-based SLAM with partial compatibility algorithm

Abstract

This article presents natural corner-based simultaneous localization and mapping (SLAM) using a new data association algorithm that achieves partial compatibility in a real unknown environment. In the proposed corners’ extraction algorithm, both the end points of an extracted line segment far away from the other segments and the intersection point of the two closer line segments are considered as corners. In data association, a partial compatibility algorithm obtaining a robust matching result with low computational complexity is proposed. This method divides all the extracted corners at every step into several groups. In each group, the local best matching vector between the extracted corners and the stored ones is found by joint compatibility, while the nearest feature for every new extracted corner is checked by individual compatibility. All these groups with the local best matching vector and the nearest feature candidate of each new extracted corner are combined, and its joint compatibility is checked with the linear matching time. The experimental results in an indoor environment with natural corners show the robust matching result and low computational complexity of the partial compatibility algorithm in comparison with individual compatibility nearest neighbor and joint compatibility branch and bound.

Weighted Conformal LiDAR-Mapping for Structured SLAM

“…where covariance Cρθ is zero, Jrα is the Jacobian matrix of line p arameters C(r, α) with respect to sensor data (ρi , θi The weighting factor ωi applied in (31) is defined in (17). To extract the parameters a, b, and c, the Lagrange Multiplier method [22] has been used, subjected to the constraint: a 2 b 2 1.…”

Simultaneous localization and mapping navigation of unmanned ground vehicle based on second-order smooth variable structure filter with improved technique to combat fading for advanced wireless communications

“…In our work, we used the Split and Merge method of line segment extraction using laser data which is largely used for extraction line based–feature process. 37–39…”

Representation of 3D Environment Map Using B-Spline Surface with Two Mutually Perpendicular LRFs

“…In order to correctly localize the mobile robot and accurately build the 2D environment map, a data association method should be used to construct the correspondence between the stored line segments and the new extracted ones. Partial compatibility algorithm (PCA) [24] has been proposed as a new robust data association algorithm with a short computation time. The output of PCA is a vector storing best matching pairs.…”

“…where :, is the th column of the control points of the extracted B-spline surface with the dimension of 3 × 1 in three-dimensional space. Matrix V and parameters {V } are calculated using the same principle as in (24) and (26). To express the uncertainty of extracted surface, the covariance matrix of the control points is propagated from the covariance matrix of the raw data points using first-order Taylor expansion:…”