LIO-PPF: Fast LiDAR-Inertial Odometry via Incremental Plane Pre-Fitting and Skeleton Tracking

As a crucial infrastructure of intelligent mobile robots, LiDAR-Inertial odometry (LIO) provides the basic capability of state estimation by tracking LiDAR scans. The high-accuracy tracking generally involves the kNN search, which is used with minimizing the point-to-plane distance. The cost for this, however, is maintaining a large local map and performing kNN plane fit for each point. In this work, we reduce both time and space complexity of LIO by saving these unnecessary costs. Technically, we design a plane pre-fitting (PPF) pipeline to track the basic skeleton of the 3D scene. In PPF, planes are not fitted individually for each scan, let alone for each point, but are updated incrementally as the scene 'flows'. Unlike kNN, the PPF is more robust to noisy and non-strict planes with our iterative Principal Component Analyse (iPCA) refinement. Moreover, a simple yet effective sandwich layer is introduced to eliminate false point-to-plane matches. Our method was extensively tested on a total number of 22 sequences across 5 open datasets, and evaluated in 3 existing state-of-the-art LIO systems. By contrast, LIO-PPF can consume only 36% of the original local map size to achieve up to 4x faster residual computing and 1.92x overall FPS, while maintaining the same level of accuracy. We fully open source our implementation at https://github.com/xingyuuchen/LIO-PPF.Comment: IROS 202

Managing financing risk in capacity investment under green supply chain competition

In this paper, we study the asymmetric duopoly models of competing supply chains with financing uncertainty. The financing uncertainty of the green supply chain’s capacity investment could be available as complete or incomplete information to the traditional supply chain. By analyzing and comparing the optimal quantities, optimal prices, and optimal profits of both cases, we find that the financing uncertainty of capacity investment does not affect either chain’s choices of equilibrium quantities and prices in the complete information case. If this information is incomplete for the traditional supply chain, financing uncertainty plays an important role in determining optimal quantities and optimal prices, together with the lending interest rate. To encourage the use of environmentally friendly technologies, government should use per-unit subsidies if the green supply chain suffers the cost disadvantage, and should encourage financial institutions to provide preferential loans to the green supply chain that suffers manufacturing or retailing capacity restrictions

An Architecture for Accountable Anonymous Access in the Internet-of-Things Network

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record.With the rapid development of the Internet, more and more devices are being connected to the Internet, making up the Internet-of-Things (IoT). The accountability and privacy are two important but contradictory factors to ensure the security of IoT networks. How to provide an accountable anonymous access to IoT networks is a challenging task. Since the IoT network is largely driven by services, in this paper we propose a new and efficient architecture to achieve accountable anonymous access to IoT networks based on services. In this architecture, a self-certifying identifier is proposed to efficiently identify a service. The efficiency and overhead of the proposed architecture are evaluated by virtue of the real trace collected from an Internet service provider. The experimental results show that the proposed architecture could efficiently balance accountability and privacy with acceptable overheads.This work is partially supported by the National Key Technology Research and Development Program (No. 2017YFB0801801), the National Science and Technology Major Project of the Ministry of Science and Technology of China (No. 2017ZX03001019), and the National Natural Science Foundation of China (No. 61672490 and No. 61303241)

APCN: A Scalable Architecture for Balancing Accountability and Privacy in Large-scale Content-based Networks

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record. Balancing accountability and privacy has become extremely important in cyberspace, and the Internet has evolved to be dominated by content transmission. Several research efforts have been devoted to contributing to either accountability or privacy protection, but none of them has managed to consider both factors in content-based networks. An efficient solution is therefore urgently demanded by service and content providers. However, proposing such a solution is very challenging, because the following questions need to be considered simultaneously: (1) How can the conflict between privacy and accountability be avoided? (2) How is content identified and accountability performed based on packets belonging to that content? (3) How can the scalability issue be alleviated on massive content accountability in large-scale networks? To address these questions, we propose the first scalable architecture for balancing Accountability and Privacy in large-scale Content-based Networks (APCN). In particular, an innovative method for identifying content is proposed to effectively distinguish the content issued by different senders and from different flows, enabling the accountability of a content based on any of its packets. Furthermore, a new idea with double-delegate (i.e., source and local delegates) is proposed to improve the performance and alleviate the scalability issue on content accountability in large-scale networks. Extensive NS-3 experiments with real trace are conducted to validate the efficiency of the proposed APCN. The results demonstrate that APCN outperforms existing related solutions in terms of lower round-trip time and higher cache hit rate under different network configurations.National Key R&D Program of ChinaNational Science and Technology Major Project of the Ministry of Science and Technology of ChinaNational Natural Science Foundation of Chin