7 accepted papers
Single-vehicle autonomy remains constrained by perception errors and uncoordinated maneuvers, resulting in avoidable collisions and throughput losses at intersections. Cooperative Driving Automation (CDA) offers substantial gains, yet fragmented toolchains hinder progress: industry develops closed s
Visual Autoregressive (AR) models generate images by predicting discrete tokens that are decoded by a visual tokenizer.Despite demonstrating strong overall image generation ability, they still underperform on text rendering with blur strokes and disrupt letter shapes. In this work, we trace this lim
Collaborative perception in unknown environments is crucial for multi-robot systems. With the emergence of foundation models, robots can now not only perceive geometric information but also achieve open-vocabulary scene understanding. However, existing map representations that support open-vocabular
Fig. 5b in [1] shows the geometric center of vertices in robots after map merging, but it shows the situation before map merging. The correct figure of Fig. 5 in [1] is shown in Fig. 1
Autonomous exploration and mapping of unknown terrains employing single or multiple robots is an essential task in mobile robotics and has therefore been widely investigated. Nevertheless, given the lack of unified data sets, metrics, and platforms to evaluate the exploration approaches, we develop…
Collaborative perception in unknown environments is a critical task for multi-robot systems. Without external positioning, multi-robot mapping systems have relied on the transfer of place recognition (PR) descriptors or sensor data for the relative pose estimation (RelPose) and share their local map
Multi-robot exploration in unknown environments is a fundamental task for a multi-robot system, involving inter-robot communication through messages among the robots. However, in a restricted communication environment, the limited communication resources become the system's bottleneck due to a large