15 accepted papers
Online state-time trajectory planning in highly dynamic environments remains an unsolved problem due to the curse of dimensionality of the state-time space. Existing state-time planners are typically implemented based on randomized sampling approaches or path searching on discrete graphs. The smooth…
Semantic information and geometrical structures of a prior map can be leveraged in visual localization to bound drift errors and improve accuracy. In this paper, we propose SemLoc, a pure visual localization system, for accurate localization in a prior semantic map. To tightly couple semantic and st…
Human activities are hugely restricted by COVID-19, recently. Robots that can conduct inter-floor navigation attract much public attention since they can substitute human workers to conduct the service work. However, current robots either depend on human assistance or elevator retrofitting, and full…
Scale ambiguity is a fundamental problem in monocular visual odometry. Typical solutions include loop closure detection and environment information mining. For applications like self-driving cars, loop closure is not always available, hence mining prior knowledge from the environment becomes a more…
Point set registration (PSR) is an essential problem in communities of computer vision, medical robotics and biomedical engineering. This paper is motivated by considering the anisotropic characteristics of the error values in estimating both the positional and orientational vectors from the PSs to…
Metastatic involvement of lymph nodes is one of the most important prognostic variables for many cancers. Several deep learning based algorithms have been developed to segment metastatic regions in pathological images to help predict prognosis. However, the training of these methods requires a large…
This paper presents a search-based partial motion planner for generating feasible trajectories of car-like robots in highly dynamic environments. The planner searches for smooth, safe, and near-time-optimal trajectories by exploring a state graph built on motion primitives. To enable fast online pla…
Object-level data association and pose estimation play a fundamental role in semantic SLAM, which remain unsolved due to the lack of robust and accurate algorithms. In this work, we propose an ensemble data associate strategy for integrating the parametric and nonparametric statistic tests. By explo…
As one of the most promising areas, mobile robots draw much attention these years. Current work in this field is often evaluated in a few manually designed scenarios, due to the lack of a common experimental platform. Meanwhile, with the recent development of deep learning techniques, some researche…
This paper presents a robust and accurate approach for the rigid registration of pre-operative and intraoperative point sets in image-guided surgery (IGS). Three challenges are identified in the pre-to-intraoperative registration: the intra-operative 3D data (usually forms a 3D curve in space) (1) i…
We present a challenging dataset, the TartanAir, for robot navigation tasks and more. The data is collected in photo-realistic simulation environments with the presence of moving objects, changing light and various weather conditions. By collecting data in simulations, we are able to obtain multi-mo…
This letter presents a novel and integrated framework for Next-Best-View (NBV) selection toward autonomous robotic exploration in indoor environments. A topological map, named semantic road map (SRM), is proposed to represent the explored environment during the exploration. The basic concept of the
Autonomous elevator operation is considered an intelligent solution in handling the inter-floor navigation problem of service robots. As one of the most fundamental steps, elevator button recognition starts to receive more and more attention. However, due to the challenging image conditions and seve…
Exploration region selection is an essential decision making process in autonomous robot exploration task. While a majority of greedy methods are proposed to deal with this problem, few efforts are made to investigate the importance of predicting long-term planning. In this paper, we present an algo…
This paper introduces a generic framework for field surveillance using consumer rotorcrafts and ground vehicles. Building such an autonomous system comes with two key challenges in persistent perception and obstacle avoidance. We begin with explaining two core algorithms to solve the challenges: an…