Adaptive experimental design (AED) methods are increasingly being used in industry as a tool to boost testing throughput or reduce experimentation cost relative to traditional A/B/N testing methods. However, the behavior and guarantees of such methods are not well-understood beyond idealized stationary settings. This paper shares lessons learned regarding the challenges of naively using AED systems in industrial settings where non-stationarity is prevalent, while also providing perspectives on the proper objectives and system specifications in such settings. We developed an AED framework for counterfactual inference based on these experiences, and tested it in a commercial environment.

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.