Metric Monocular Localization Using Signed Distance Fields

Metric localization plays a critical role in vision-based navigation. For overcoming the degradation of matching photometry under appearance changes, recent research resorted to introducing geometry constraints of the prior scene structure. In this paper, we present a metric localization method for the monocular camera, using the Signed Distance Field (SDF) as a global map representation. Leveraging the volumetric distance information from SDFs, we aim to relax the assumption of an accurate structure from the local Bundle Adjustment (BA) in previous methods. By tightly coupling the distance factor with temporal visual constraints, our system corrects the odometry drift and jointly optimizes global camera poses with the local structure. We validate the proposed approach on both indoor and outdoor public datasets. Compared to the state-of-the-art methods, it achieves a comparable performance with a minimal sensor configuration.Comment: Accepted to 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS

FSNet: Redesign Self-Supervised MonoDepth for Full-Scale Depth Prediction for Autonomous Driving

Predicting accurate depth with monocular images is important for low-cost robotic applications and autonomous driving. This study proposes a comprehensive self-supervised framework for accurate scale-aware depth prediction on autonomous driving scenes utilizing inter-frame poses obtained from inertial measurements. In particular, we introduce a Full-Scale depth prediction network named FSNet. FSNet contains four important improvements over existing self-supervised models: (1) a multichannel output representation for stable training of depth prediction in driving scenarios, (2) an optical-flow-based mask designed for dynamic object removal, (3) a self-distillation training strategy to augment the training process, and (4) an optimization-based post-processing algorithm in test time, fusing the results from visual odometry. With this framework, robots and vehicles with only one well-calibrated camera can collect sequences of training image frames and camera poses, and infer accurate 3D depths of the environment without extra labeling work or 3D data. Extensive experiments on the KITTI dataset, KITTI-360 dataset and the nuScenes dataset demonstrate the potential of FSNet. More visualizations are presented in \url{https://sites.google.com/view/fsnet/home}Comment: 12 pages. conditionally accepted by IEEE T-AS

A novel GFP nude rat model to investigate tumor-stroma interactions

Backgroud: A key strategy for the study of the tumor microenvironment is to implant human tumor cells in an immunodeficient rodent strain ubiquitously expressing a fluorescent marker. Here, a novel nude rat expressing a green fluorescent protein (GFP) transgene was established and engrafted with primary human tumor tissue in order to separate tumor from stromal cell populations for subsequent molecular analysis. Methods: SD-TG (GFP) 2BalRrrc transgenic rats were crossed with HsdHan™: rnu/rnu Rowett nude rats to develop a GFP expressing immunocompromised rat. PCR and flow cytometry were used to follow the GFP genotype and phenotype in newborns. After three to four generations, animals were implanted with 4 T1 dsRed murine breast cancer cells or primary human glioblastoma (GBM) biopsies to generate xenografts for subsequent separation by fluorescence-activated cell sorting (FACS). Results: Fluorecence microscopy and reverse transcription-PCR demonstrated that GFP, under the control of the human ubiquitin C promoter, was stably maintained and expressed in diverse organs over several generations. Immunophenotyping of blood samples by flow cytometry confirmed that the immunodeficient features of the parental rat strain, HsdHan™: rnu/rnu, were retained in the GFP nude rat. Both the murine cell line and human GBM biopsies engrafted, and stromal cell populations were isolated from dissociated xenografts by FACS to > 95% purity. Conclusions: A GFP transgene was stably introduced into a nude rat background in which human and murine cancer cells successfully engrafted. This animal strain provides a novel in vivo system for detailed cellular and molecular characterization of tumor-stroma interactions.publishedVersio

ATG-PVD: Ticketing Parking Violations on A Drone

In this paper, we introduce a novel suspect-and-investigate framework, which can be easily embedded in a drone for automated parking violation detection (PVD). Our proposed framework consists of: 1) SwiftFlow, an efficient and accurate convolutional neural network (CNN) for unsupervised optical flow estimation; 2) Flow-RCNN, a flow-guided CNN for car detection and classification; and 3) an illegally parked car (IPC) candidate investigation module developed based on visual SLAM. The proposed framework was successfully embedded in a drone from ATG Robotics. The experimental results demonstrate that, firstly, our proposed SwiftFlow outperforms all other state-of-the-art unsupervised optical flow estimation approaches in terms of both speed and accuracy; secondly, IPC candidates can be effectively and efficiently detected by our proposed Flow-RCNN, with a better performance than our baseline network, Faster-RCNN; finally, the actual IPCs can be successfully verified by our investigation module after drone re-localization.Comment: 17 pages, 11 figures and 3 tables. This paper is accepted by ECCV Workshops 202