This paper takes advantage of the complementary characteristics of Global Positioning System (GPS) and Light Detection and Ranging (LiDAR) to provide periodic corrections to Inertial Navigation System (INS) alternatively in different environmental conditions. In open sky, where GPS signals are available and LiDAR measurements are sparse, GPS is integrated with INS. Meanwhile, in confined outdoor environments and indoors, where GPS is unreliable or unavailable and LiDAR measurements are rich, LiDAR replaces GPS to integrate with INS. This paper also proposes an innovative hybrid scan matching algorithm that combines the feature-based scan matching method and Iterative Closest Point (ICP) based scan matching method. The algorithm can work and transit between two modes depending on the number of matched line features over two scans, thus achieving efficiency and robustness concurrently. Two integration schemes of INS and LiDAR with hybrid scan matching algorithm are implemented and compared. Real experiments are performed on an Unmanned Ground Vehicle (UGV) for both outdoor and indoor environments. Experimental results show that the multi-sensor integrated system can remain sub-meter navigation accuracy during the whole trajectory.

No abstract

Common root rot, caused by Bipolaris sorokiniana, is one of the most prevalent diseases of wheat and has led to major declines in wheat yield and quality worldwide. Here, strain XZ34-1 was isolated from soil and identified as Bacillus amyloliquefaciens based on the morphological, physiological, biochemical characteristics and 16S rDNA sequence. Culture filtrate (CF) of strain XZ34-1 showed a high inhibition rate against B.sorokiniana and had a broad antifungal spectrum. It also remarkably inhibited the mycelial growth and spore germination of B. sorokiniana. In pot control experiments, the incidence and disease index of common root rot in wheat seedlings were decreased after treatment with CF, and the biological control efficacy was significant, up to 78.24%. Further studies showed XZ34-1 could produce antifungal bioactive substances and had the potential of promoting plant growth. Lipopeptide genes detection with PCR indicated that strain XZ34-1 may produce lipopeptides. Furthermore, activities of defense-related enzymes were enhanced in wheat seedlings after inoculation with B.sorokiniana and treatment with CF, which showed induced resistance could be produced in wheat to resist pathogens. These results reveal that strain XZ34-1 is a promising candidate for application as a biological control agent against B.sorokiniana.

Autonomous Unmanned Ground Vehicles (UGVs) require a reliable navigation system that works in all environments. However, indoor navigation remains a challenge because the existing satellite-based navigation systems such as the Global Positioning System (GPS) are mostly unavailable indoors. In this paper, a tightly-coupled integrated navigation system that integrates two dimensional (2D) Light Detection and Ranging (LiDAR), Inertial Navigation System (INS), and odometry is introduced. An efficient LiDAR-based line features detection/tracking algorithm is proposed to estimate the relative changes in orientation and displacement of the vehicle. Furthermore, an error model of INS/odometry system is derived. LiDAR-estimated orientation/position changes are fused by an Extended Kalman Filter (EKF) with those predicted by INS/odometry using the developed error model. Errors estimated by EKF are used to correct the position and orientation of the vehicle and to compensate for sensor errors. The proposed system is verified through simulation and real experiment on an UGV equipped with LiDAR, MEMS-based IMU, and encoder. Both simulation and experimental results showed that sensor errors are accurately estimated and the drifts of INS are significantly reduced leading to navigation performance of sub-metre accuracy. K E Y