Although Global Navigation Satellite Systems (GNSSs) generally provide adequate accuracy for outdoor localization, this is not the case for indoor environments, due to signal obstruction. Therefore, a self-contained localization scheme is beneficial under such circumstances. Modern sensors and algorithms endow moving robots with the capability to perceive their environment, and enable the deployment of novel localization schemes, such as odometry, or Simultaneous Localization and Mapping (SLAM). The former focuses on incremental localization, while the latter stores an interpretable map of the environment concurrently. In this context, this paper conducts a comprehensive review of sensor modalities, including Inertial Measurement Units (IMUs), Light Detection and Ranging (LiDAR), radio detection and ranging (radar), and cameras, as well as applications of polymers in these sensors, for indoor odometry. Furthermore, analysis and discussion of the algorithms and the fusion frameworks for pose estimation and odometry with these sensors are performed. Therefore, this paper straightens the pathway of indoor odometry from principle to application. Finally, some future prospects are discussed.

To improve the energy-efficiency of transport systems, it is necessary to investigate electric trains with on-board hybrid energy storage devices (HESDs), which are applied to assist the traction and recover the regenerative energy. In this paper, a time-based mixed-integer linear programming (MILP) model is proposed to obtain the energy-saving operation for electric trains with different constraints of on-board HESDs, such as their capacity, initial state of charge (SOC), and level of degradation. The proposed integrated power flow model based on the train longitudinal dynamics, power split of on-board HESDs, and line impedance is discretized and linearized, aiming to minimize the net energy consumption (NEC). The results reveal that on-board HESDs with a higher capacity does not necessarily lead to a higher energy-saving rate; a lower or excessive initial SOC could undermine the energy-saving potential; considering the long-term train operation, the degradation of the Li-ion battery will influence the energy-saving operation for electric trains, as well as result in an energy-saving rate that ranges from 41.57% to 31.90%. The practical data from Guangzhou Metro Line 7 were applied in the simulations, which enhanced the practicality and effectiveness of the proposed method.

Due to their high flexibility, low cost, and energy-saving advantages, applying Unmanned Aerial Vehicles (UAVs) in logistics is a promising field to achieve better social and economic benefits. Since UAVs’ energy storage capacity is generally low, it is essential to reduce energy costs to improve their system’s energy efficiency. In this paper, we proposed a novel trajectory planning framework to achieve the optimal trajectory with the minimum amount of energy consumption under the constraints of obstacles in a static environment. Based on UAV dynamics, we first derived the required power functions of multi-rotor UAVs in vertical and horizontal flight. To generate a feasible trajectory, we first adopted the A* algorithm to find a path and developed a safe flight corridor for the UAV to fly across by expanding the waypoints against the environment, and then proposed a time-discretization method to formulate the trajectory generation problem and solve it by the convex optimization algorithm. The optimization results in a static environment with obstacles demonstrated that the proposed method could efficiently and effectively obtain the optimal trajectory with the minimum amount of energy consumption under different allowed mission times and payloads. The framework would promote a variety of logistics UAV applications relevant to trajectory planning.