Radar-on-Lidar: metric radar localization on prior lidar maps

Radar and lidar, provided by two different range sensors, each has pros and cons of various perception tasks on mobile robots or autonomous driving. In this paper, a Monte Carlo system is used to localize the robot with a rotating radar sensor on 2D lidar maps. We first train a conditional generative adversarial network to transfer raw radar data to lidar data, and achieve reliable radar points from generator. Then an efficient radar odometry is included in the Monte Carlo system. Combining the initial guess from odometry, a measurement model is proposed to match the radar data and prior lidar maps for final 2D positioning. We demonstrate the effectiveness of the proposed localization framework on the public multi-session dataset. The experimental results show that our system can achieve high accuracy for long-term localization in outdoor scenes

LocNet: Global localization in 3D point clouds for mobile vehicles

Global localization in 3D point clouds is a challenging problem of estimating the pose of vehicles without any prior knowledge. In this paper, a solution to this problem is presented by achieving place recognition and metric pose estimation in the global prior map. Specifically, we present a semi-handcrafted representation learning method for LiDAR point clouds using siamese LocNets, which states the place recognition problem to a similarity modeling problem. With the final learned representations by LocNet, a global localization framework with range-only observations is proposed. To demonstrate the performance and effectiveness of our global localization system, KITTI dataset is employed for comparison with other algorithms, and also on our long-time multi-session datasets for evaluation. The result shows that our system can achieve high accuracy.Comment: 6 pages, IV 2018 accepte

Communication constrained cloud-based long-term visual localization in real time

Visual localization is one of the primary capabilities for mobile robots. Long-term visual localization in real time is particularly challenging, in which the robot is required to efficiently localize itself using visual data where appearance may change significantly over time. In this paper, we propose a cloud-based visual localization system targeting at long-term localization in real time. On the robot, we employ two estimators to achieve accurate and real-time performance. One is a sliding-window based visual inertial odometry, which integrates constraints from consecutive observations and self-motion measurements, as well as the constraints induced by localization on the cloud. This estimator builds a local visual submap as the virtual observation which is then sent to the cloud as new localization constraints. The other one is a delayed state Extended Kalman Filter to fuse the pose of the robot localized from the cloud, the local odometry and the high-frequency inertial measurements. On the cloud, we propose a longer sliding-window based localization method to aggregate the virtual observations for larger field of view, leading to more robust alignment between virtual observations and the map. Under this architecture, the robot can achieve drift-free and real-time localization using onboard resources even in a network with limited bandwidth, high latency and existence of package loss, which enables the autonomous navigation in real-world environment. We evaluate the effectiveness of our system on a dataset with challenging seasonal and illuminative variations. We further validate the robustness of the system under challenging network conditions

Synthesis and Evaluation of Novel Organogermanium Sesquioxides As Antitumor Agents

Five new organogermanium sesquioxides have been synthesized and characterized by elemental analysis and IR spectra. All the compounds were tested for antitumor activities against KB, HCT, and Bel cells in vitro. Compound 5 (γ-thiocarbamido propyl germanium sesquioxide) showed excellent antitumor activity, and its inhibition yield to KB, HCT, and Bel cells was 92.9%, 84.9%, and 70.9%, respectively. A rapid method was described for the labeling compound 5 with 99mTc, and the optimum labeling conditions were investigated. The labeling yield is above 90% in pH 7.0, 20°C, reaction time greater than 10 minutes, 1 mg of compound 5, and 0.075∼0.1 mg of SnCl2. The biodistribution of 99mTc labeled compound 5 in nude mice bearing human colonic xenografts was studied. The result showed that the tumor uptakes were 0.73, 0.97, 0.87, and 0.62 ID%/g at 1-hour, 3-hour, 6-hour, and 20-hour postinjection, respectively. T/NT (the uptake ratio for per gram of tumor over normal tissues) was 18.3 for tumor versus brain and 5.81 for tumor versus muscle at 20-hour postinjection. The tumor clearance was slow. The results showed that compound 5 may be developed to be a suitable cancer therapeutic agent

Synthesis and Broad-Spectrum Antiviral Activity of Some Novel Benzo-Heterocyclic Amine Compounds

A series of novel unsaturated five-membered benzo-heterocyclic amine derivatives were synthesized and assayed to determine their in vitro broad-spectrum antiviral activities. The biological results showed that most of our synthesized compounds exhibited potent broad-spectrum antiviral activity. Notably, compounds 3f (IC50 = 3.21–5.06 μM) and 3g (IC50 = 0.71–34.87 μM) showed potent activity towards both RNA viruses (influenza A, HCV and Cox B3 virus) and a DNA virus (HBV) at low micromolar concentrations. An SAR study showed that electron-withdrawing substituents located on the aromatic or heteroaromatic ring favored antiviral activity towards RNA viruses