Visual loop closure detection plays a key role in navigation systems for intelligent vehicles. Nowadays, state-of-the-art algorithms are focused on unidirectional loop closures, but there are situations where they are not sufficient for identifying previously visited places. Therefore, the detection of bidirectional loop closures when a place is revisited in a different direction provides a more robust visual navigation. We propose a novel approach for identifying bidirectional loop closures on panoramic image sequences. Our proposal combines global binary descriptors and a matching strategy based on cross-correlation of sub-panoramas, which are defined as the different parts of a panorama. A set of experiments considering several binary descriptors (ORB, BRISK, FREAK, LDB) is provided, where LDB excels as the most suitable. The proposed matching proffers a reliable bidirectional loop closure detection, which is not efficiently solved in any other previous research. Our method is successfully validated and compared against FAB-MAP and BRIEF-Gist. The Ford Campus and the Oxford New College datasets are considered for evaluation.