Precision Comparaison of Trimble SX12 against MOCAP

Project Context: REISAR & France 2030 This work is part of the REISAR project (Advanced Robotic System for Sewer Network Inspection and Water Preservation), funded under the ANR-23-DMRO-0014 reference and supported by the France 2030 investment plan. The REISAR initiative addresses major challenges in clean water access and sustainable resource management, in alignment with the United Nations' Sustainable Development Goals for 2030. Sewer networks, essential for wastewater transport, are often aging, difficult to access, and hazardous for maintenance personnel. Starting in 2026, French regulations will require precise mapping (accuracy < 40 cm) of underground structures in sensitive areas, expanding to all urban zones by 2030. This project contributes directly to meeting these standards by validating high-precision localization technologies. REISAR proposes an innovative robotic solution: a ground-based intelligent robot capable of navigating and inspecting sewer tunnels with minimal visual features. The project explores: Robust localization in low-texture environments Semantic data transmission from robots User-friendly interfaces for non-experts Training and operational safety improvements The consortium(Pilgrim Technology , TRAAK , Conscience Robotics), ensuring a multidisciplinary approach. The “Made in France” nature of the solution highlights national technological excellence and its adaptability to other demanding environments. Technical Contribution: ROS2-Based Validation Framework To support the REISAR objectives, this project provides a ROS2-based framework for evaluating and comparing the accuracy of the Trimble SX12 total station against a high-precision motion capture (Mocap) system. The main objective is to validate the SX12 as a reliable ground truth reference for robotics and localization research. The package includes tools for: Visualizing and transforming trajectory data Analyzing static and dynamic evaluation scenarios Summary Table of Test Results The table below summarizes the main performance metrics for the three dynamic tests as well as the static test. Test Type Test Name RMSE (mm) MAE (mm) Max Error (mm) Mean Speed (m/s) Mean Accel. (m/s²) Max Accel. (m/s²) Dynamic Test Spiral Movement(Test1) 17.25 15.16 90.22 0.20 0.708 0.708 Zigzag MovementTest2) 47.13 40.38 349.12 0.20 1.996 5.614 Slalom(Test3) 24.73 23.08 89.82 0.20 0.708 3.354 Static Test 4 Static Points 4.72 4.56 6.73 - - - For reproducibility and further analysis, three ROS bags are attached below, each corresponding to a specific dynamic test (spiral, zigzag, and slalom movements). These datasets illustrate the exact experimental conditions and results discussed in the study.项目背景：REISAR与法国2030投资计划 本研究隶属于REISAR项目（用于管网巡检与水保护的先进机器人系统，Advanced Robotic System for Sewer Network Inspection and Water Preservation），该项目以编号ANR-23-DMRO-0014获得资助，并得到法国2030投资计划的支持。 REISAR计划契合联合国2030年可持续发展目标，旨在解决清洁水获取与可持续资源管理领域的重大挑战。下水道管网作为废水输送的核心基础设施，普遍存在老化、难以进入且维护人员作业风险较高的问题。 自2026年起，法国法规将要求敏感区域内的地下构筑物实现精度优于40厘米的精准测绘，并于2030年将该要求扩展至所有城市区域。本项目通过验证高精度定位技术，直接助力满足上述标准要求。 REISAR提出了一项创新性机器人解决方案：一款可在视觉特征极少的环境中自主导航并完成隧道巡检的地面智能机器人。本项目将探索以下研究方向： 1. 低纹理环境下的鲁棒定位 2. 机器人端语义数据传输 3. 面向非专业用户的友好交互界面 4. 运维培训与作业安全优化 本项目由Pilgrim Technology、TRAAK、Conscience Robotics组成的项目联盟实施，确保采用多学科研究路径。该方案的「法国制造」属性彰显了国家技术实力，同时其技术方案也可适配其他高要求作业场景。 技术贡献：基于ROS2的验证框架 为支撑REISAR项目目标，本项目搭建了一套基于ROS2的验证框架，用于评估并对比Trimble SX12全站仪与高精度动作捕捉（Mocap）系统的定位精度。本框架的核心目标是验证SX12作为机器人与定位研究领域可靠真值参考的可行性。 该工具包包含以下功能模块： - 轨迹数据可视化与坐标转换 - 静态与动态评估场景分析 测试结果汇总表 下表汇总了三项动态测试与一项静态测试的核心性能指标： | 测试类型 | 测试名称 | 均方根误差(RMSE, mm) | 平均绝对误差(MAE, mm) | 最大误差(mm) | 平均速度(m/s) | 平均加速度(m/s²) | 最大加速度(m/s²) | | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | | 动态测试 | 螺旋运动（Test1） | 17.25 | 15.16 | 90.22 | 0.20 | 0.708 | 0.708 | | 动态测试 | 锯齿形运动（Test2） | 47.13 | 40.38 | 349.12 | 0.20 | 1.996 | 5.614 | | 动态测试 | 回转运动（Test3） | 24.73 | 23.08 | 89.82 | 0.20 | 0.708 | 3.354 | | 静态测试 | 4个静态测点 | 4.72 | 4.56 | 6.73 | - | - | - | 为保障实验可复现性与后续分析需求，本研究附带三个ROS bag数据包，分别对应三项动态测试场景（螺旋运动、锯齿形运动与回转运动）。这些数据集完整呈现了本研究讨论的实验条件与测试结果。