本文约 3 5 0 0 字，推荐阅读时长 1 0 分钟，欢迎关注新财富公众号。 成本扣到分的物流行业对无人化的需求最大 2025年上半年我国社会物流总费用与GDP的比率为14%，较2024年同期下降0.2个百分点，节约物流费用约1300亿元。根据政策目标2027年将比率降至 13.5%左右。尽管从2006年正式建立统计以来，目前已经是最低水平，但是与发达国家7%-9%的比率相比，我国物流费用还有比较大的优化空间。 卡车的L4级别自动驾驶与卡车最大的拥有者，卡车司机的利益相悖，国内70%卡车运力是个人，对个人来说，自身的体力劳动成本趋近于0，这也是个体 户比运营公司成本占优的主要原因。无人化无疑是革了卡车司机的命，而运营公司本身成本就比不过个体，自然也不愿意在无人化上投入更多成本冒 险。 万亿规模的物流快递行业，对于新技术带来的降本增效布局都会非常积极，每年在运输方面花费的成本高达千亿，面对激烈的竞争，单票快递的成本往 往要计算到分以下，而作为50%以上人力成本占比的行业，无人化的降本潜力巨大。 推动物流领域无人化、自动化是降本增效的关键手段，无人车和移动机器人技术是智慧物流领域战略的重要组成部分。无人车包括低 ...

作者丨 量子位 编辑丨 量子位 点击下方 卡片 ，关注" 具身智能之心 "公众号 >> 点击进入→ 具身 智能之心 技术交流群 更多干货，欢迎加入国内首个具身智能全栈学习社区 ： 具身智能之心知识星球 (戳我) ， 这里包含所有你想要的。 户外SLAM的尺度漂移问题，终于有了新解法！ 香港科技大学（广州） 的研究的最新成果： S3PO-GS ，一个专门针对户外单目SLAM的3D高斯框架，已被ICCV 2025接收。 项工作的亮点在于首次实现了RGB单目SLAM的全局尺度一致性。在Waymo、KITTI和DL3DV三大户外基准测试中，S3PO-GS不仅在新视角 合成任务中刷新了SOTA纪录，更是在DL3DV场景中将跟踪误差降低了77.3%。 这篇文章做了什么？ 在自动驾驶、机器人导航及AR/VR等前沿领域，SLAM技术的鲁棒性直接影响系统性能。 当前基于3D高斯（3DGS）的SLAM方案虽在室内场景表现卓越，但在仅依赖RGB输入的无界户外环境中仍面临严峻挑战： 单目系统固有的深度先验缺失导致几何信息不足，而引入单目深度估计或端到端点云模型（如MASt3R）作为几何先验时，又因帧间尺度不一 致性引发系统级尺度漂移 ...

Core Viewpoint - The article discusses the innovative S3PO-GS framework developed by Hong Kong University of Science and Technology (Guangzhou) to address the scale drift problem in outdoor monocular SLAM, achieving global scale consistency for RGB monocular SLAM [1][4][21]. Group 1: Introduction to SLAM and Challenges - SLAM technology's robustness is crucial for performance in fields like autonomous driving, robotic navigation, and AR/VR [2]. - Current 3D Gaussian-based SLAM solutions excel in indoor environments but face significant challenges in unbounded outdoor settings due to the inherent lack of depth prior in monocular systems, leading to geometric information insufficiency [3]. Group 2: S3PO-GS Framework - The S3PO-GS framework is designed to achieve global scale consistency in RGB monocular SLAM, addressing the dual challenges of scale drift and geometric prior deficiency [4][21]. - The framework incorporates three core technological breakthroughs: 1. A self-consistent tracking module that generates scale-consistent 3D point clouds and establishes accurate 2D-3D correspondences to eliminate drift errors in pose estimation [5]. 2. A dynamic mapping mechanism that introduces a local patch-based scale alignment algorithm to dynamically calibrate the scale parameters of pre-trained point clouds with the 3D Gaussian scene [5]. 3. A joint optimization architecture that synchronously enhances localization accuracy and scene reconstruction quality through point cloud replacement strategies and geometric supervision loss functions [5]. Group 3: Experimental Results - In benchmark tests on Waymo, KITTI, and DL3DV datasets, S3PO-GS demonstrated significant advantages, surpassing all existing 3D Gaussian SLAM methods, particularly reducing tracking error by 77.3% in the DL3DV scene [5][21]. - The PSNR metric for the Waymo dataset reached 26.73, setting a new standard for real-time high-precision reconstruction in unbounded outdoor scenes [5][21]. Group 4: Methodology and Mechanisms - The S3PO-GS system begins with a map initialization phase, optimizing a pre-trained point cloud through 1000 iterations to construct an initial 3D Gaussian scene representation [6]. - During the tracking phase, the system rasterizes and renders the 3D Gaussian point cloud of adjacent keyframes, establishing 2D-3D correspondences to estimate scale-consistent camera poses [8]. - The dynamic mapping mechanism utilizes a local patch-based scale alignment algorithm to achieve precise calibration by analyzing block similarity and selecting high-confidence points [9][12]. Group 5: Future Directions - The research indicates that S3PO-GS reduces the number of iterations required for pose estimation to 10% of traditional methods, achieving accurate camera tracking in complex datasets like Waymo [21]. - Future work will explore loop closure detection and large-scale dynamic scene optimization to expand the application boundaries of this method in outdoor SLAM [23].

Core Viewpoint - The article announces the launch of the "Black Warrior Series 001," a lightweight autonomous driving solution aimed at research and education, with a promotional price of 34,999 yuan and a deposit scheme for early orders [1]. Group 1: Product Overview - The "Black Warrior 001" is developed by the Autonomous Driving Heart team, featuring a comprehensive solution that supports perception, localization, fusion, navigation, and planning, built on an Ackermann chassis [2]. - The product is designed for various educational and research applications, including undergraduate learning, graduate research, and as teaching tools in laboratories and vocational schools [5]. Group 2: Performance and Testing - The product has been tested in multiple environments, including indoor, outdoor, and parking scenarios, demonstrating its capabilities in perception, localization, fusion, navigation, and planning [3]. - Specific tests include 3D point cloud target detection, 2D and 3D laser mapping in indoor parking, and outdoor scene mapping, including night driving capabilities [7][9][11][15][17]. Group 3: Hardware Specifications - Key hardware components include: - 3D LiDAR: Mid 360 - 2D LiDAR: Lidar from Raysun - Depth Camera: Orbbec with IMU - Main Control Chip: Nvidia Orin NX 16G - Display: 1080p [19]. - The vehicle specifications include a weight of 30 kg, a battery power of 50W, a voltage of 24V, and a maximum speed of 2 m/s [21]. Group 4: Software and Functionality - The software framework includes ROS, C++, and Python, supporting one-click startup and providing a development environment [23]. - The system supports various functionalities such as 2D and 3D SLAM, vehicle navigation, and obstacle avoidance [24]. Group 5: After-Sales and Support - The company offers one year of after-sales support for non-human damage, with free repairs for damages caused by operational errors or code modifications during the warranty period [46].

Core Viewpoint - The article discusses the surge of robotics companies, particularly focusing on Stand Robot's IPO ambitions and the broader trend of robotics firms seeking to go public in Hong Kong's specialized technology sector. Group 1: Stand Robot's IPO Journey - Stand Robot submitted its prospectus to the Hong Kong Stock Exchange on June 23, 2025, aiming to become the "first industrial embodiment intelligent stock" [4] - The company is currently the fifth largest provider of industrial intelligent mobile robot solutions globally and the fourth in industrial embodiment intelligent robots by sales volume as of December 31, 2024 [4] - Stand Robot's founder, Wang Yongkun, has a background in robotics and aims to enhance production efficiency and reduce costs for enterprises through their SLAM technology [15][14] Group 2: Industry Trends and Other IPOs - Multiple robotics companies, including Woan Robot, XianGong Intelligent, and Yunji Technology, have also initiated IPO processes, indicating a growing trend in the industry [5][26] - The market for humanoid robots and automation equipment is expected to reach a scale of 100,000 to 200,000 units, supporting the growth of domestic manufacturers [28] - Stand Robot's revenue grew from 96.3 million yuan in 2022 to 162.2 million yuan in 2023, with a projected increase to 250.5 million yuan in 2024, reflecting a compound annual growth rate of 61.3% [30] Group 3: Investment and Financing - Stand Robot has completed four rounds of financing, achieving a valuation of 2.1 billion yuan [16] - The company has attracted significant investments from notable firms, including Xiaomi and Bohua Capital, which are essential for meeting the requirements for the specialized technology listing [24][22] - The robotics sector has seen a surge in investment, with companies like Yushun Technology completing substantial financing rounds, indicating a robust interest in the industry [31]