12 accepted papers
Visual Place Recognition (VPR) is crucial for autonomous vehicles, as it enables their identification of previously visited locations. Compared with conventional single-frame retrieval, leveraging sequences of frames to depict places has been proven effective in alleviating perceptual aliasing. Howe
As delivery robots are increasingly integrated into our daily lives, their ability to navigate through crowded spaces demands swift and accurate prediction of pedestrian trajectories, which is crucial for autonomous functionality. However, existing methods face challenges of unstable accuracy and in
As a new sensor, 4D radar (x, y, z, velocity) has great potential for V2X, due to its 3D point cloud, direct doppler velocity output, long distance ranging, low-cost, and more importantly, robust perception in all weathers. However, the extrinsic calibration of multiple long baseline 4D radars is ra…
LiDAR-based SLAM may easily fail in adverse weathers (e.g., rain, snow, smoke, fog), while mmWave Radar remains unaffected. However, current researches are primarily focused on 2D (x,y)(x,y) or 3D (x, yx, y, doppler) Radar and 3D LiDAR, while limited work can be found for 4D Radar (x, y, zx, y, z, d…
Robust visual place recognition (VPR) against significant appearance changes is crucial for the life-long operation of mobile robots. Focusing on this task, we propose a Co-Attentive Hierarchical Image Representations (CAHIR) framework for VPR, which unifies attention-sharing global and local descri…
In V2X (Vehicle-to-Everything), one important work is to extrinsically calibrate multiple 3D LiDARs, which are mounted with a long baseline and large viewpoint-difference at the road-side. Current solutions either require a specific target being set up (e.g., a sphere), or require specific features…
Last-mile delivery robots are usually required to navigate on the sidewalk through a fixed route. The current solutions heavily rely on the image-based perception and GPS localization to successfully complete delivery tasks. However, it is prone to fail and become unreliable when the robot runs in c…
Symmetric environment is one of the most intractable and challenging scenarios for mobile robots to accomplish global localization tasks, due to the highly similar geometrical structures and insufficient distinctive features. Existing localization solutions in such scenarios either depend on pre-dep…
In unstructured and unknown environments, heterogeneous robots must be able to perceive the environment, coordinate with each other and complete tasks collaboratively with onboard sensors. In this paper, a tightly-coupled perception and navigation framework is proposed for heterogeneous land-air rob…
Performing collaborative semantic mapping is a critical challenge for cooperative robots to maintain a comprehensive contextual understanding of the surroundings. Most of the existing work either focus on single robot semantic mapping or collaborative geometry mapping. In this paper, a novel hierarc…
In order to enable a team of robots to operate successfully, retrieving accurate relative transformation between robots is the fundamental requirement. So far, most research on relative localization mainly focus on geometry features such as points, lines and planes. To address this problem, collabor…
Enabling long-term operation during day and night for collaborative robots requires a comprehensive understanding of the unstructured environment. Besides, in the dynamic environment, robots must be able to recognize dynamic objects and collaboratively build a global map. This paper proposes a novel…