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Manganese‐based aqueous batteries utilizing Mn2+/MnO2 redox reactions are promising choices for grid‐scale energy storage due to their high theoretical specific capacity, high power capability, low‐cost, and intrinsic safety with water‐based electrolytes. However, the application of such systems is hindered by the insulating nature of deposited MnO2, resulting in low normalized areal loading (0.005–0.05 mAh cm−2) during the charge/discharge cycle. In this work, the electrochemical performance of various MnO2 polymorphs in Mn2+/MnO2 redox reactions is investigated, and ɛ‐MnO2 with low conductivity is determined to be the primary electrochemically deposited phase in normal acidic aqueous electrolyte. It is found that increasing the temperature can change the deposited phase from ɛ‐MnO2 with low conductivity to γ‐MnO2 with two order of magnitude increase in conductivity. It is demonstrated that the highly conductive γ‐MnO2 can be effectively exploited for ultrahigh areal loading electrode, and a normalized areal loading of 33 mAh cm−2 is achieved. At a mild temperature of 50 °C, cells are cycled with an ultrahigh areal loading of 20 mAh cm−2 (1–2 orders of magnitude higher than previous studies) for over 200 cycles with only 13% capacity loss.

The traditional map-matching algorithms mainly use two methods: the incremental method and the global method. Both of them have advantages and disadvantages of themselves: while the global map-matching algorithm produces better matching results, the incremental algorithm produces results of lower quality faster. All things considering the two traditional algorithms, this paper proposes a heuristic map-matching algorithm by using vector-based recognition. Firstly, the algorithm uses the heuristic search method which is similar to A* algorithm, and it makes use of geometric operation to form the restriction, and make the comparison between the vector formed with the vehicular GPS points and the special road network to heuristicly search and select the vehicular possible traveling routes. Secondly, it globally compares the vehicular every possible route by calculating the map-matching weight, and then chooses the optimal one. The result of testing demonstrates the efficiency of the algorithm both at accuracy and computational speed when handling the large-scale data of GPS tracking data even under the complex road network conditions.

Estimating the absolute pose of a camera is one of the key steps for computer vision. In some cases, especially when using a wide-angle or zoom lens, the focal length and radial distortion also need to be considered. Therefore, in this paper, an efficient and robust method for a single solution is proposed to estimate the absolute pose for a camera with unknown focal length and radial distortion, using three 2D–3D point correspondences and known camera position. The problem is decomposed into two sub-problems, which makes the estimation simpler and more efficient. The first sub-problem is to estimate the focal length and radial distortion. An important geometric characteristic of radial distortion, that the orientation of the 2D image point with respect to the center of distortion (i.e., principal point in this paper) under radial distortion is unchanged, is used to solve this sub-problem. The focal length and up to four-order radial distortion can be determined with this geometric characteristic, and it can be applied to multiple distortion models. The values with no radial distortion are used as the initial values, which are close to the global optimal solutions. Then, the sub-problem can be efficiently and accurately solved with the initial values. The second sub-problem is to determine the absolute pose with geometric linear constraints. After estimating the focal length and radial distortion, the undistorted image can be obtained, and then the absolute pose can be efficiently determined from the point correspondences and known camera position using the undistorted image. Experimental results indicate this method’s accuracy and numerical stability for pose estimation with unknown focal length and radial distortion in synthetic data and real images.

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