RadarSLAM: Radar based Large-Scale SLAM in All Weathers

Numerous Simultaneous Localization and Mapping (SLAM) algorithms have been presented in last decade using different sensor modalities. However, robust SLAM in extreme weather conditions is still an open research problem. In this paper, RadarSLAM, a full radar based graph SLAM system, is proposed for reliable localization and mapping in large-scale environments. It is composed of pose tracking, local mapping, loop closure detection and pose graph optimization, enhanced by novel feature matching and probabilistic point cloud generation on radar images. Extensive experiments are conducted on a public radar dataset and several self-collected radar sequences, demonstrating the state-of-the-art reliability and localization accuracy in various adverse weather conditions, such as dark night, dense fog and heavy snowfall

Cross-Dimensional Refined Learning for Real-Time 3D Visual Perception from Monocular Video

We present a novel real-time capable learning method that jointly perceives a 3D scene's geometry structure and semantic labels. Recent approaches to real-time 3D scene reconstruction mostly adopt a volumetric scheme, where a Truncated Signed Distance Function (TSDF) is directly regressed. However, these volumetric approaches tend to focus on the global coherence of their reconstructions, which leads to a lack of local geometric detail. To overcome this issue, we propose to leverage the latent geometric prior knowledge in 2D image features by explicit depth prediction and anchored feature generation, to refine the occupancy learning in TSDF volume. Besides, we find that this cross-dimensional feature refinement methodology can also be adopted for the semantic segmentation task by utilizing semantic priors. Hence, we proposed an end-to-end cross-dimensional refinement neural network (CDRNet) to extract both 3D mesh and 3D semantic labeling in real time. The experiment results show that this method achieves a state-of-the-art 3D perception efficiency on multiple datasets, which indicates the great potential of our method for industrial applications.Comment: Accpeted to ICCV 2023 Workshops. Project page: https://hafred.github.io/cdrnet

Radar-based localization and mapping for large-scale environments and adverse weather conditions

In mobile robotics, localization and mapping is one of the fundamental capabilities towards autonomy. Navigating autonomously in large-scale, unstructured, extreme and dynamical environments is particularly challenging due to the high variations in the scene. To deliver a robotic system that can operate 24/7 in outdoor environment, we need to design a state estimation system that is robust in all weather conditions. In this thesis, we propose, implement and validate three systems to tackle the problem of long-term localization and mapping. We focus on using radar-only platform to realize the SLAM and localization systems in probabilistic manners. We first introduce a radar-based SLAM system that can operate in city-scale environment. Second, we present an improved version of the radar-based SLAM system with enhanced odometry estimation capability and extensive experiments on extreme weather conditions, proving that our proposed radar SLAM solution is viable in all weather conditions. We also demonstrate the superiority of radar-based SLAM system compared to LiDAR and vision based system in snowy and low light conditions respectively. Finally, we show how to combine online public maps and radar sensor to achieve accurate localization even we do not have a prior sensor map. We show that our proposed localization system can generalize to different scenarios and we validate it across three datasets collected in three different continents

Meta-Analysis on Efficacy of Vaccination against Staphylococcus aureus and Escherichia coli

Mastitis is a common disease responsible for the biggest economic loss in the dairy industry. Antibiotic therapy does not provide long-term protection. And residue is a major concern in food safety. Vaccination is an alternative control method with great potential for bovine mastitis. Our study focus on evaluating vaccine efficacy regarding reducing the incidence of clinical and subclinical mastitis. Meta-analysis was used to pool data extracted from previous studies. 26 records from 13 studies were examined. A fixed effect model was constructed assigning incidence as the measurement of the outcome. Risk ratio (RR) was the parameter that measured the incidence differences between treated group and control group. Studies and records were categorised based on vaccine antigens. In vaccine against Staphylococcus aureus, RR was 0.76; 95% CI (0.65,0.89), while in vaccine against Escherichia coli RR was 0.96; 95% CI (0.86,1.08)