Domestic attempts to advance the Sustainable Development Goals (SDGs) in a country can have synergistic and/or trade-off effects on the advancement of SDGs in other countries. Transboundary SDG interactions can be delivered through various transmission channels (e.g., trade, river flow, ocean currents, and air flow). This study quantified the transboundary interactions through these channels between 768 pairs of SDG indicators. The results showed that although high income countries only comprised 14.18% of the global population, they contributed considerably to total SDG interactions worldwide (60.60%). Transboundary synergistic effects via international trade were 14.94% more pronounced with trade partners outside their immediate geographic vicinity than with neighbouring ones. Conversely, nature-caused flows (including river flow, ocean currents, and air flow) resulted in 39.29% stronger transboundary synergistic effects among neighboring countries compared to non-neighboring ones. To facilitate the achievement of SDGs worldwide, it is essential to enhance collaboration among countries and leverage transboundary synergies.

We propose a framework for odometry, mapping and ground segmentation using a backpack LiDAR system that achieves both real-time and low-drift performance. First, we present a spatio-temporal calibration method to carefully merge scans from the two laser scanners on a backpack. Second, we propose a feature extraction method which generalizes a point's geometrical characteristics as two groups (disjoint, continuous) and three types (edge, corner, plane). The extracted features are used in point cloud registration in the odometry and mapping tasks. Third, a fast ground segmentation method is customized for the backpack LiDAR system. Finally, we evaluate the proposed method in four datasets logged by the backpack across different scales and environments. Furthermore, NTU VIRAL dataset is used to benchmark our method quantitatively. Experiments show that our method consistently outperforms the state-of-the-art methods before using loop closure optimization and sensor fusion techniques.

Seamless localization service is regarded as an essential requirement towards smart city based applications. The performance of seamless localization is restricted by the limited coverage of indoor navigation database and disturbed results of the location sources in complex urban environments. This paper proposes a map-assisted seamless localization framework that uses the self-constructed navigation database and Bi-directional Long Short-Term Memory (Bi-LSTM) based signal quality control criteria (SL-SDBQ). Daily-life trajectories provided by the inertial odometry are optimized and calibrated by the map-originated indoor pedestrian network information for self-constructing the crowdsourced navigation database, and the Bi-LSTM is applied for quality evaluation of different location sources. In addition, the robust Kalman filter is adopted to integrate the information of inertial odometry, crowdsourced Wi-Fi fingerprinting, Global Navigation Satellite System (GNSS), and signal quality evaluation results, and the error ellipse is applied for map matching to further enhance the performance of multi-source fusion based seamless positioning. The experimental results prove that the proposed SL-SDBQ can achieve autonomous and precise indoor and outdoor positioning performance, and meter-level localization precision can be realized under the assistance of indoor map.

Loop closure detection is a critical yet still open technique to enhance the performance of Simultaneous Localization and Mapping (SLAM). In this paper, a very deep and lightweight neural network DeLightLCD is proposed to enable efficient loop closure detection. The architecture of the network contains two key modules: a siamese feature extraction module and a dual-attention-based feature difference module. A very deep but lightweight feature extraction network is designed to extract high-dimensional and discriminative features. The dual-attention-based feature difference network generates feature difference maps to identify the difference between a pair of LiDAR scans. Besides, a loop closure candidate rapid selection method is proposed to extract loop closure candidates from the LiDAR scan sequence. The workflow enables real-time loop closure detection in large-scale environments. This approach was evaluated on the open-source KITTI odometry benchmark and Ford campus dataset. The experimental results show that this method outperforms the state-of-the-art methods. Although the model was trained only on the KITTI dataset, it also demonstrated superior performance on the Ford campus dataset. In particular, the proposed approach is more lightweight and highly efficient than the current existing approaches.