18 accepted papers
Autonomous Underwater Vehicles (AUVs) encounter significant challenges in confined spaces like ports and testing tanks, where vehicle-environment interactions, such as wave reflections and unsteady flows, introduce complex, time-varying disturbances. Model-based state estimation methods can struggle
Image-goal navigation enables a robot to reach the location where a target image was captured, using visual cues for guidance. However, current methods either rely heavily on data and computationally expensive learning-based approaches or lack efficiency in complex environments due to insufficient e
Radar SLAM is robust in challenging conditions, such as fog, dust, and smoke, but suffers from the sparsity and noisiness of radar sensing, including speckle noise and multipath effects. This study provides a performance-enhanced radar SLAM system by incorporating point uncertainty. The basic system
Existing indoor SLAM datasets primarily focus on robot sensing, often lacking building architectures. To address this gap, we design and construct the first dataset to couple the SLAM and BIM, named SLABIM. This dataset provides BIM and SLAM -oriented sensor data, both modeling a university building
This study presents a system, TripletLoc, for fast and robust global registration of a single LiDAR scan to a large-scale reference map. In contrast to conventional methods using place recognition and point cloud registration, TripletLoc directly generates correspondences on lightweight semantics, w
In this study, we present a novel simultaneous localization and mapping (SLAM) system, VIMS, designed for underwater navigation. Conventional visual-inertial state estimators encounter significant practical challenges in perceptually degraded underwater environments, particularly in scale estimation
Radar offers the advantage of providing additional physical properties related to observed objects. In this study, we design a physical-enhanced radar-inertial odometry system that capitalizes on the Doppler velocities and radar cross-section information. The filter for static radar points, correspo…
In this paper, we present a centralized framework for multi-session LiDAR mapping in urban environments, by utilizing lightweight line and plane map representations instead of widely used point clouds. The proposed framework achieves consistent mapping in a coarse-to-fine manner. Global place recogn…
In this study, we introduce an online monocular lane mapping approach that solely relies on a single camera and odometry for generating spline-based maps. Our proposed technique models the lane association process as an assignment issue utilizing a bipartite graph, and assigns weights to the edges b…
Global point cloud registration is essential in many robotics tasks like loop closing and relocalization. Unfortunately, the registration often suffers from the low overlap between point clouds, a frequent occurrence in practical applications due to occlusion and viewpoint change. In this paper, we…
In this study, we propose a novel system for real-time coloring LiDAR point clouds with a low-cost RS camera. The main challenges are dealing with the motion distortion of the RS camera and the multi-sensor time synchronization. To tackle these challenges, we carefully design a hardware synchronizer…
Trajectory planning is a critical component in autonomous vehicles directly responsible for driving safety and efficiency during deployment. The ability to find the optimal trajectory in real-time is critical for autonomous driving. This paper presents a novel general framework using the Fast Iterat
LiDAR-based global localization is a fundamental problem for mobile robots. It consists of two stages, place recognition and pose estimation, which yields the current orientation and translation, using only the current scan as query and a database of map scans. Inspired by the definition of a recogn…
Global localization and kidnapping are two challenging problems in robot localization. The popular method, Monte Carlo Localization (MCL) addresses the problem by iteratively updating a set of particles with a “sampling-weighting” loop. Sampling is decisive to the performance of MCL [1]. However, tr
Global localization is essential for robot navigation, of which the first step is to retrieve a query from the map database. This problem is called place recognition. In recent years, LiDAR scan based place recognition has drawn attention as it is robust against the appearance change. In this letter
In-flight objects capture is extremely challenging. The robot is required to complete trajectory prediction, interception position calculation and motion planning within tens of milliseconds. As in-flight uneven objects are affected by various kinds of forces, which leads to the time-varying acceler…
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 pro…
Long-term visual localization in outdoor environment is a challenging problem, especially faced with the cross-seasonal, bi-directional tasks and changing environment. In this paper we propose a novel visual inertial localization framework that localizes against the LiDAR-built map. Based on the geo…