This paper investigates the design of channel estimation and 3D localization algorithms in a challenging scenario, where a sub-connected planar extremely large-scale multiple-input multiple-output (XL-MIMO) communicates with multi-antenna users. In the near field, the uplink MIMO channel is of full column rank and therefore can not be estimated effectively by applying existing codebooks that are designed for the far-field case or for the near-field case but limited to single antenna users. To solve this problem, we propose a three-stage algorithm aided by orthogonal matching pursuit (OMP) and sparse Bayesian learning (SBL). Specifically, we firstly partition the XL-MIMO into subarrays and use OMP to solve the compressed sensing (CS) problem about subarray channel estimation with the Discrete Fourier Transform (DFT)-based dictionary matrix. Secondly, exploiting the estimated subarray channels and employing one-dimensional multiple signal classification (MUSIC), we estimate the central location of the user array under the Least Squares (LS) criterion. Finally, we utilize the estimated central location to construct a refined location-aided dictionary matrix and obtain the MIMO channel estimation using SBL. Results exhibit the significant superiority of the proposed algorithm compared with several benchmarks, in terms of both the pilot overhead and estimation accuracy.