Robot Perception of Static and Dynamic Objects with an Autonomous Floor Scrubber

Self-Improving Semantic Perception for Indoor Localisation

We propose a novel robotic system that can improve its perception during deployment. Contrary to the established approach of learning semantics from large datasets and deploying fixed models, we propose a framework in which semantic models are continuously updated on the robot to adapt to the deployment environments. By combining continual learning with self-supervision, our robotic system learns online during deployment without external supervision. We conduct real-world experiments with robots localising in 3D floorplans. Our experiments show how the robot's semantic perception improves duri…

编码关键信息：面向自动驾驶节能感知的语义感知型内存接口

Coding What Matters: A Semantic-Aware Memory Interface for Energy-Efficient Perception in Autonomous Vehicles

提出语义感知型内存接口编码器MotiMem-Omega，通过分配图像块精度层级保护关键交通信息，可降低内存接口能量，同时保留高感知精度。

面向服务机器人高效安全导航的视觉辅助在线 A* 路径规划

Vision-Aided Online A* Path Planning for Efficient and Safe Navigation of Service Robots

本文提出将轻量级语义分割与在线 A* 规划器紧密集成，把用户定义的视觉约束投影为全局地图上的非几何障碍，使低成本服务机器人能够依据上下文重要性在复杂环境中实现实时、安全且高效的导航。

借助大语言模型为室内场景构建分层3D场景图以实现智能空间感知

Intelligent Spatial Perception by Building Hierarchical 3D Scene Graphs for Indoor Scenarios with the Help of LLMs

针对智能机器人导航对空间环境全面理解的需求，提出利用大语言模型构建含多层级节点的分层3D场景图的系统，通过LLM投票机制提升房间标注准确性，融合语义与几何数据生成精准环境表征，助力情境感知导航与任务规划。

An A* Curriculum Approach to Reinforcement Learning for RGBD Indoor Robot Navigation

Training robots to navigate diverse environments is a challenging problem as it involves the confluence of several different perception tasks such as mapping and localization, followed by optimal path-planning and control. Recently released photo-realistic simulators such as Habitat allow for the training of networks that output control actions directly from perception: agents use Deep Reinforcement Learning (DRL) to regress directly from the camera image to a control output in an end-to-end fashion. This is data-inefficient and can take several days to train on a GPU. Our paper tries to overc…

从开阔水域到封闭舱室：ProteusVPR用于海洋感知与舱室检查中的跨场景视觉位置识别

From Open Waters to Enclosed Cabins: ProteusVPR for Cross-Scene Visual Place Recognition in Maritime Perception and Cabin Inspection

提出ProteusVPR两阶段检索-细化框架，通过几何-视觉估计网络融合时序帧与几何描述符，在跨场景海洋环境下将平均定位误差降低60%以上。

重新思考移动机器人对室内场所的语义分类

Rethinking the semantic classification of indoor places by mobile robots

本文提出了一种新的语义分类方法，通过允许房间内的混淆来提高移动机器人对室内场所的理解能力。

Modeling Curiosity in a Mobile Robot for Long-Term Autonomous Exploration and Monitoring

This paper presents a novel approach to modeling curiosity in a mobile robot, which is useful for monitoring and adaptive data collection tasks, especially in the context of long term autonomous missions where pre-programmed missions are likely to have limited utility. We use a realtime topic modeling technique to build a semantic perception model of the environment, using which, we plan a path through the locations in the world with high semantic information content. The life-long learning behavior of the proposed perception model makes it suitable for long-term exploration missions. We valid…

米洛，一款完全自主的室内/室外机器人导盲犬

Milo, a Fully Autonomous Indoor/Outdoor Robotic Guide Dog

研究旨在开发机器人导盲犬平台，核心方法是构建含改装机器人、感知与导航堆栈的系统，主要贡献是实现完全自主、室内外可用的米洛平台，开源软硬件，且导航表现优于基线。

Skyeye Team at MBZIRC 2020: A team of aerial and ground robots for GPS-denied autonomous fire extinguishing in an urban building scenario

The paper presents a framework for fire extinguishing in an urban scenario by a team of aerial and ground robots. The system was developed to address Challenge 3 of the 2020Mohamed Bin Zayed International Robotics Challenge (MBZIRC). The challenge required to autonomously detect, locate and extinguish fires on different floors of a building, as well as in its surroundings. The multi-robot system developed consists of a heterogeneous robot team of up to three Unmanned Aerial Vehicles (UAV) and one Unmanned Ground Vehicle (UGV). We describe the main hardware and software components for UAV and U…