Visual-Inertial Odometry(VIO) fuses measurements from camera and Inertial Measurement Unit(IMU) to achieve accumulative performance that is better than using individual sensors.Hybrid VIO is an extended Kalman filter-...Visual-Inertial Odometry(VIO) fuses measurements from camera and Inertial Measurement Unit(IMU) to achieve accumulative performance that is better than using individual sensors.Hybrid VIO is an extended Kalman filter-based solution which augments features with long tracking length into the state vector of Multi-State Constraint Kalman Filter(MSCKF). In this paper, a novel hybrid VIO is proposed, which focuses on utilizing low-cost sensors while also considering both the computational efficiency and positioning precision. The proposed algorithm introduces several novel contributions. Firstly, by deducing an analytical error transition equation, onedimensional inverse depth parametrization is utilized to parametrize the augmented feature state.This modification is shown to significantly improve the computational efficiency and numerical robustness, as a result achieving higher precision. Secondly, for better handling of the static scene,a novel closed-form Zero velocity UPda Te(ZUPT) method is proposed. ZUPT is modeled as a measurement update for the filter rather than forbidding propagation roughly, which has the advantage of correcting the overall state through correlation in the filter covariance matrix. Furthermore, online spatial and temporal calibration is also incorporated. Experiments are conducted on both public dataset and real data. The results demonstrate the effectiveness of the proposed solution by showing that its performance is better than the baseline and the state-of-the-art algorithms in terms of both efficiency and precision. A related software is open-sourced to benefit the community.展开更多
Feature detection and Tracking, which heavily rely on the gray value information of images, is a very importance procedure for Visual-Inertial Odometry (VIO) and the tracking results significantly affect the accuracy ...Feature detection and Tracking, which heavily rely on the gray value information of images, is a very importance procedure for Visual-Inertial Odometry (VIO) and the tracking results significantly affect the accuracy of the estimation results and the robustness of VIO. In high contrast lighting condition environment, images captured by auto exposure camera shows frequently change with its exposure time. As a result, the gray value of the same feature in the image show vary from frame to frame, which poses large challenge to the feature detection and tracking procedure. Moreover, this problem further been aggravated by the nonlinear camera response function and lens attenuation. However, very few VIO methods take full advantage of photometric camera calibration and discuss the influence of photometric calibration to the VIO. In this paper, we proposed a robust monocular visual-inertial odometry, PC-VINS-Mono, which can be understood as an extension of the opens-source VIO pipeline, VINS-Mono, with the capability of photometric calibration. We evaluate the proposed algorithm with the public dataset. Experimental results show that, with photometric calibration, our algorithm achieves better performance comparing to the VINS-Mono.展开更多
Visual-Inertial Odometry(VIO)has been developed from Simultaneous Localization and Mapping(SLAM)as a lowcost and versatile sensor fusion approach and attracted increasing attention in ground vehicle positioning.Howeve...Visual-Inertial Odometry(VIO)has been developed from Simultaneous Localization and Mapping(SLAM)as a lowcost and versatile sensor fusion approach and attracted increasing attention in ground vehicle positioning.However,VIOs usually have the degraded performance in challenging environments and degenerated motion scenarios.In this paper,we propose a ground vehicle-based VIO algorithm based on the Multi-State Constraint Kalman Filter(MSCKF)framework.Based on a unifed motion manifold assumption,we derive the measurement model of manifold constraints,including velocity,rotation,and translation constraints.Then we present a robust flter-based algorithm dedicated to ground vehicles,whose key is the real-time manifold noise estimation and adaptive measurement update.Besides,GNSS position measurements are loosely coupled into our approach,where the transformation between GNSS and VIO frame is optimized online.Finally,we theoretically analyze the system observability matrix and observability measures.Our algorithm is tested on both the simulation test and public datasets including Brno Urban dataset and Kaist Urban dataset.We compare the performance of our algorithm with classical VIO algorithms(MSCKF,VINS-Mono,R-VIO,ORB_SLAM3)and GVIO algorithms(GNSS-MSCKF,VINS-Fusion).The results demonstrate that our algorithm is more robust than other compared algorithms,showing a competitive position accuracy and computational efciency.展开更多
This paper proposes a Visual-Inertial Odometry(VIO)algorithm that relies solely on monocular cameras and Inertial Measurement Units(IMU),capable of real-time self-position estimation for robots during movement.By inte...This paper proposes a Visual-Inertial Odometry(VIO)algorithm that relies solely on monocular cameras and Inertial Measurement Units(IMU),capable of real-time self-position estimation for robots during movement.By integrating the optical flow method,the algorithm tracks both point and line features in images simultaneously,significantly reducing computational complexity and the matching time for line feature descriptors.Additionally,this paper advances the triangulation method for line features,using depth information from line segment endpoints to determine their Plcker coordinates in three-dimensional space.Tests on the EuRoC datasets show that the proposed algorithm outperforms PL-VIO in terms of processing speed per frame,with an approximate 5%to 10%improvement in both relative pose error(RPE)and absolute trajectory error(ATE).These results demonstrate that the proposed VIO algorithm is an efficient solution suitable for low-computing platforms requiring real-time localization and navigation.展开更多
Although VSLAM/VISLAM has achieved great success,it is still difficult to quantitatively evaluate the localization results of different kinds of SLAM systems from the aspect of augmented reality due to the lack of an ...Although VSLAM/VISLAM has achieved great success,it is still difficult to quantitatively evaluate the localization results of different kinds of SLAM systems from the aspect of augmented reality due to the lack of an appropriate benchmark.For AR applications in practice,a variety of challenging situations(e.g.,fast motion,strong rotation,serious motion blur,dynamic interference)may be easily encountered since a home user may not carefully move the AR device,and the real environment may be quite complex.In addition,the frequency of camera lost should be minimized and the recovery from the failure status should be fast and accurate for good AR experience.Existing SLAM datasets/benchmarks generally only provide the evaluation of pose accuracy and their camera motions are somehow simple and do not fit well the common cases in the mobile AR applications.With the above motivation,we build a new visual-inertial dataset as well as a series of evaluation criteria for AR.We also review the existing monocular VSLAM/VISLAM approaches with detailed analyses and comparisons.Especially,we select 8 representative monocular VSLAM/VISLAM approaches/systems and quantitatively evaluate them on our benchmark.Our dataset,sample code and corresponding evaluation tools are available at the benchmark website http://www.zjucvg.net/eval-vislam/.展开更多
Embodied visual exploration is critical for building intelligent visual agents. This paper presents the neural exploration with feature-based visual odometry and tracking-failure-reduction policy(Ne OR), a framework f...Embodied visual exploration is critical for building intelligent visual agents. This paper presents the neural exploration with feature-based visual odometry and tracking-failure-reduction policy(Ne OR), a framework for embodied visual exploration that possesses the efficient exploration capabilities of deep reinforcement learning(DRL)-based exploration policies and leverages feature-based visual odometry(VO) for more accurate mapping and positioning results. An improved local policy is also proposed to reduce tracking failures of feature-based VO in weakly textured scenes through a refined multi-discrete action space, keyframe fusion, and an auxiliary task. The experimental results demonstrate that Ne OR has better mapping and positioning accuracy compared to other entirely learning-based exploration frameworks and improves the robustness of feature-based VO by significantly reducing tracking failures in weakly textured scenes.展开更多
Since its introduction in 2014,the LiDAR odometry and mapping(LOAM)algorithm has become a cornerstone in the fields of autonomous driving and intelligent robotics.LOAM provides robust support for autonomous navigation...Since its introduction in 2014,the LiDAR odometry and mapping(LOAM)algorithm has become a cornerstone in the fields of autonomous driving and intelligent robotics.LOAM provides robust support for autonomous navigation in complex dynamic environments through precise localization and environmental mapping.This paper offers a comprehensive review of the innovations and optimizations made to the LOAM algorithm,covering advancements in multi-sensor fusion technology,frontend processing optimization,backend optimization,and loop closure detection.These improvements have significantly enhanced LOAM's performance in various scenarios,including urban,agricultural,and underground environments.However,challenges remain in areas such as data synchronization,real-time processing,computational complexity,and environmental adaptability.Looking ahead,future developments are expected to focus on creating more efficient multi-sensor fusion algorithms,expanding application domains,and building more robust systems,thereby driving continued progress in autonomous driving,intelligent robotics,and autonomous unmanned systems.展开更多
Although deep learning methods have been widely applied in slam visual odometry(VO)over the past decade with impressive improvements,the accuracy remains limited in complex dynamic environments.In this paper,a composi...Although deep learning methods have been widely applied in slam visual odometry(VO)over the past decade with impressive improvements,the accuracy remains limited in complex dynamic environments.In this paper,a composite mask-based generative adversarial network(CMGAN)is introduced to predict camera motion and binocular depth maps.Specifically,a perceptual generator is constructed to obtain the corresponding parallax map and optical flow between two neighboring frames.Then,an iterative pose improvement strategy is proposed to improve the accuracy of pose estimation.Finally,a composite mask is embedded in the discriminator to sense structural deformation in the synthesized virtual image,thereby increasing the overall structural constraints of the network model,improving the accuracy of camera pose estimation,and reducing drift issues in the VO.Detailed quantitative and qualitative evaluations on the KITTI dataset show that the proposed framework outperforms existing conventional,supervised learning and unsupervised depth VO methods,providing better results in both pose estimation and depth estimation.展开更多
近年来,深度学习技术在移动机器人同时定位与建图(Simultaneous localization and mapping,SLAM)领域取得了显著进展,为解决传统视觉SLAM在动态环境下面临的挑战提供了新的思路.本文首先总结了传统视觉SLAM在预处理、视觉里程计以及闭...近年来,深度学习技术在移动机器人同时定位与建图(Simultaneous localization and mapping,SLAM)领域取得了显著进展,为解决传统视觉SLAM在动态环境下面临的挑战提供了新的思路.本文首先总结了传统视觉SLAM在预处理、视觉里程计以及闭环检测模块的局限性.随后,聚焦于深度学习在视觉SLAM中的应用,重点介绍了基于深度学习的预处理、视觉里程计和闭环检测模块,以及其如何提升视觉SLAM的鲁棒性和精度.最后,探讨了基于深度学习SLAM面临的挑战并展望了未来研究方向,包括轻量化网络设计、场景的长期建模以及自监督学习等,以推动深度学习SLAM在实际应用中的落地.展开更多
在车辆高速剧烈运动场景下,现有激光雷达-惯性里程计(LiDAR-inertial odometry,LIO)因IMU前向传播误差的快速累积,导致车辆的运动畸变补偿精度下降,进而引发"补偿误差-配准误差-状态估计误差"的级联效应,最终造成车辆定位轨...在车辆高速剧烈运动场景下,现有激光雷达-惯性里程计(LiDAR-inertial odometry,LIO)因IMU前向传播误差的快速累积,导致车辆的运动畸变补偿精度下降,进而引发"补偿误差-配准误差-状态估计误差"的级联效应,最终造成车辆定位轨迹显著偏离真实状态,本文提出了基于迭代误差卡尔曼滤波(iterated error-state Kalman filter,IESKF)的自适应激光雷达-惯性里程计(state-adaptive update LiDAR-inertial odometry,SAU-LIO)。首先,提出基于协方差特征值阈值的动态调整策略,以实时监测LIO误差累积趋势,自适应缩短状态更新时间间隔,有效抑制剧烈运动下的误差发散;其次,结合线特征与面特征的联合提取策略,构建概率观测模型,通过观测协方差矩阵约束实现不同置信度特征的最优加权融合,实现环境特征的有效利用。最后,基于NCLT(the university of Michigan north campus long-term vision and LIDAR dataset)、UTBM(EU long-term dataset with multiple sensors for autonomous driving)标准数据集及实车试验平台的验证结果表明:SAU-LIO算法在保证实时性的前提下,与对比算法相比具有更高的定位精度,在低速工况下,平均定位误差较次优的对比算法减小14.3%,在组合工况下,平均定位误差较次优的对比算法减小9.4%。展开更多
Odometry using incremental wheel encoder odometry suffers from the accumulation of kinematic sensors provides the relative robot pose estimation. However, the modeling errors of wheels as the robot＇s travel distance ...Odometry using incremental wheel encoder odometry suffers from the accumulation of kinematic sensors provides the relative robot pose estimation. However, the modeling errors of wheels as the robot＇s travel distance increases. Therefore, the systematic errors need to be calibrated. The University of Michigan Benchmark（UMBmark） method is a widely used calibration scheme of the systematic errors in two wheel differential mobile robots. In this paper, the accurate parameter estimation of systematic errors is proposed by extending the conventional method. The contributions of this paper can be summarized as two issues. The first contribution is to present new calibration equations that reduce the systematic odometry errors. The new equations were derived to overcome the limitation of conventional schemes. The second contribu tion is to propose the design guideline of the test track for calibration experiments. The calibration performance can be im proved by appropriate design of the test track. The simulations and experimental results show that the accurate parameter es timation can be implemented by the proposed method.展开更多
基金supported by the National Key Research and Development Program of China(Nos.2016YFB0502004,2017YFC0821102)。
文摘Visual-Inertial Odometry(VIO) fuses measurements from camera and Inertial Measurement Unit(IMU) to achieve accumulative performance that is better than using individual sensors.Hybrid VIO is an extended Kalman filter-based solution which augments features with long tracking length into the state vector of Multi-State Constraint Kalman Filter(MSCKF). In this paper, a novel hybrid VIO is proposed, which focuses on utilizing low-cost sensors while also considering both the computational efficiency and positioning precision. The proposed algorithm introduces several novel contributions. Firstly, by deducing an analytical error transition equation, onedimensional inverse depth parametrization is utilized to parametrize the augmented feature state.This modification is shown to significantly improve the computational efficiency and numerical robustness, as a result achieving higher precision. Secondly, for better handling of the static scene,a novel closed-form Zero velocity UPda Te(ZUPT) method is proposed. ZUPT is modeled as a measurement update for the filter rather than forbidding propagation roughly, which has the advantage of correcting the overall state through correlation in the filter covariance matrix. Furthermore, online spatial and temporal calibration is also incorporated. Experiments are conducted on both public dataset and real data. The results demonstrate the effectiveness of the proposed solution by showing that its performance is better than the baseline and the state-of-the-art algorithms in terms of both efficiency and precision. A related software is open-sourced to benefit the community.
基金support from National Natural Science Foundation of China (No.61375086)Key Project (No.KZ201610005010) of S&T Plan of Beijing Municipal Commission of EducationBeijing Natural Science Foundation(4174083).
文摘Feature detection and Tracking, which heavily rely on the gray value information of images, is a very importance procedure for Visual-Inertial Odometry (VIO) and the tracking results significantly affect the accuracy of the estimation results and the robustness of VIO. In high contrast lighting condition environment, images captured by auto exposure camera shows frequently change with its exposure time. As a result, the gray value of the same feature in the image show vary from frame to frame, which poses large challenge to the feature detection and tracking procedure. Moreover, this problem further been aggravated by the nonlinear camera response function and lens attenuation. However, very few VIO methods take full advantage of photometric camera calibration and discuss the influence of photometric calibration to the VIO. In this paper, we proposed a robust monocular visual-inertial odometry, PC-VINS-Mono, which can be understood as an extension of the opens-source VIO pipeline, VINS-Mono, with the capability of photometric calibration. We evaluate the proposed algorithm with the public dataset. Experimental results show that, with photometric calibration, our algorithm achieves better performance comparing to the VINS-Mono.
基金the National Nature Science Foundation of China(NSFC)under Grant No.62273229the Equipment PreResearch Field Foundation under Grant No.80913010303.
文摘Visual-Inertial Odometry(VIO)has been developed from Simultaneous Localization and Mapping(SLAM)as a lowcost and versatile sensor fusion approach and attracted increasing attention in ground vehicle positioning.However,VIOs usually have the degraded performance in challenging environments and degenerated motion scenarios.In this paper,we propose a ground vehicle-based VIO algorithm based on the Multi-State Constraint Kalman Filter(MSCKF)framework.Based on a unifed motion manifold assumption,we derive the measurement model of manifold constraints,including velocity,rotation,and translation constraints.Then we present a robust flter-based algorithm dedicated to ground vehicles,whose key is the real-time manifold noise estimation and adaptive measurement update.Besides,GNSS position measurements are loosely coupled into our approach,where the transformation between GNSS and VIO frame is optimized online.Finally,we theoretically analyze the system observability matrix and observability measures.Our algorithm is tested on both the simulation test and public datasets including Brno Urban dataset and Kaist Urban dataset.We compare the performance of our algorithm with classical VIO algorithms(MSCKF,VINS-Mono,R-VIO,ORB_SLAM3)and GVIO algorithms(GNSS-MSCKF,VINS-Fusion).The results demonstrate that our algorithm is more robust than other compared algorithms,showing a competitive position accuracy and computational efciency.
文摘This paper proposes a Visual-Inertial Odometry(VIO)algorithm that relies solely on monocular cameras and Inertial Measurement Units(IMU),capable of real-time self-position estimation for robots during movement.By integrating the optical flow method,the algorithm tracks both point and line features in images simultaneously,significantly reducing computational complexity and the matching time for line feature descriptors.Additionally,this paper advances the triangulation method for line features,using depth information from line segment endpoints to determine their Plcker coordinates in three-dimensional space.Tests on the EuRoC datasets show that the proposed algorithm outperforms PL-VIO in terms of processing speed per frame,with an approximate 5%to 10%improvement in both relative pose error(RPE)and absolute trajectory error(ATE).These results demonstrate that the proposed VIO algorithm is an efficient solution suitable for low-computing platforms requiring real-time localization and navigation.
基金the National Key Research and Development Program of China(2016YFB1001501)NSF of China(61672457)+1 种基金the Fundamental Research Funds for the Central Universities(2018FZA5011)Zhejiang University-SenseTime Joint Lab of 3D Vision.
文摘Although VSLAM/VISLAM has achieved great success,it is still difficult to quantitatively evaluate the localization results of different kinds of SLAM systems from the aspect of augmented reality due to the lack of an appropriate benchmark.For AR applications in practice,a variety of challenging situations(e.g.,fast motion,strong rotation,serious motion blur,dynamic interference)may be easily encountered since a home user may not carefully move the AR device,and the real environment may be quite complex.In addition,the frequency of camera lost should be minimized and the recovery from the failure status should be fast and accurate for good AR experience.Existing SLAM datasets/benchmarks generally only provide the evaluation of pose accuracy and their camera motions are somehow simple and do not fit well the common cases in the mobile AR applications.With the above motivation,we build a new visual-inertial dataset as well as a series of evaluation criteria for AR.We also review the existing monocular VSLAM/VISLAM approaches with detailed analyses and comparisons.Especially,we select 8 representative monocular VSLAM/VISLAM approaches/systems and quantitatively evaluate them on our benchmark.Our dataset,sample code and corresponding evaluation tools are available at the benchmark website http://www.zjucvg.net/eval-vislam/.
基金supported by the National Natural Science Foundation of China (No.62202137)the China Postdoctoral Science Foundation (No.2023M730599)the Zhejiang Provincial Natural Science Foundation of China (No.LMS25F020009)。
文摘Embodied visual exploration is critical for building intelligent visual agents. This paper presents the neural exploration with feature-based visual odometry and tracking-failure-reduction policy(Ne OR), a framework for embodied visual exploration that possesses the efficient exploration capabilities of deep reinforcement learning(DRL)-based exploration policies and leverages feature-based visual odometry(VO) for more accurate mapping and positioning results. An improved local policy is also proposed to reduce tracking failures of feature-based VO in weakly textured scenes through a refined multi-discrete action space, keyframe fusion, and an auxiliary task. The experimental results demonstrate that Ne OR has better mapping and positioning accuracy compared to other entirely learning-based exploration frameworks and improves the robustness of feature-based VO by significantly reducing tracking failures in weakly textured scenes.
基金supported by Jiangsu Agriculture Science and Technology Innovation Fund(CX(23)2003)China Agriculture Research System of MOF and MARA(CARS-28-21)+2 种基金the National Natural Science Foundation of China(32201680)the National Science and Technology Development Program of China(NK2022160104)the National Key Research and Development Program of China(2022YFD2001400).
文摘Since its introduction in 2014,the LiDAR odometry and mapping(LOAM)algorithm has become a cornerstone in the fields of autonomous driving and intelligent robotics.LOAM provides robust support for autonomous navigation in complex dynamic environments through precise localization and environmental mapping.This paper offers a comprehensive review of the innovations and optimizations made to the LOAM algorithm,covering advancements in multi-sensor fusion technology,frontend processing optimization,backend optimization,and loop closure detection.These improvements have significantly enhanced LOAM's performance in various scenarios,including urban,agricultural,and underground environments.However,challenges remain in areas such as data synchronization,real-time processing,computational complexity,and environmental adaptability.Looking ahead,future developments are expected to focus on creating more efficient multi-sensor fusion algorithms,expanding application domains,and building more robust systems,thereby driving continued progress in autonomous driving,intelligent robotics,and autonomous unmanned systems.
基金supported by the Program of Graduate Education and Teaching Reform in Tianjin University of Technology(Nos.YBXM2204 and ZDXM2202)the National Natural Science Foundation of China(Nos.62203331 and 62103299)。
文摘Although deep learning methods have been widely applied in slam visual odometry(VO)over the past decade with impressive improvements,the accuracy remains limited in complex dynamic environments.In this paper,a composite mask-based generative adversarial network(CMGAN)is introduced to predict camera motion and binocular depth maps.Specifically,a perceptual generator is constructed to obtain the corresponding parallax map and optical flow between two neighboring frames.Then,an iterative pose improvement strategy is proposed to improve the accuracy of pose estimation.Finally,a composite mask is embedded in the discriminator to sense structural deformation in the synthesized virtual image,thereby increasing the overall structural constraints of the network model,improving the accuracy of camera pose estimation,and reducing drift issues in the VO.Detailed quantitative and qualitative evaluations on the KITTI dataset show that the proposed framework outperforms existing conventional,supervised learning and unsupervised depth VO methods,providing better results in both pose estimation and depth estimation.
文摘近年来,深度学习技术在移动机器人同时定位与建图(Simultaneous localization and mapping,SLAM)领域取得了显著进展,为解决传统视觉SLAM在动态环境下面临的挑战提供了新的思路.本文首先总结了传统视觉SLAM在预处理、视觉里程计以及闭环检测模块的局限性.随后,聚焦于深度学习在视觉SLAM中的应用,重点介绍了基于深度学习的预处理、视觉里程计和闭环检测模块,以及其如何提升视觉SLAM的鲁棒性和精度.最后,探讨了基于深度学习SLAM面临的挑战并展望了未来研究方向,包括轻量化网络设计、场景的长期建模以及自监督学习等,以推动深度学习SLAM在实际应用中的落地.
文摘在车辆高速剧烈运动场景下,现有激光雷达-惯性里程计(LiDAR-inertial odometry,LIO)因IMU前向传播误差的快速累积,导致车辆的运动畸变补偿精度下降,进而引发"补偿误差-配准误差-状态估计误差"的级联效应,最终造成车辆定位轨迹显著偏离真实状态,本文提出了基于迭代误差卡尔曼滤波(iterated error-state Kalman filter,IESKF)的自适应激光雷达-惯性里程计(state-adaptive update LiDAR-inertial odometry,SAU-LIO)。首先,提出基于协方差特征值阈值的动态调整策略,以实时监测LIO误差累积趋势,自适应缩短状态更新时间间隔,有效抑制剧烈运动下的误差发散;其次,结合线特征与面特征的联合提取策略,构建概率观测模型,通过观测协方差矩阵约束实现不同置信度特征的最优加权融合,实现环境特征的有效利用。最后,基于NCLT(the university of Michigan north campus long-term vision and LIDAR dataset)、UTBM(EU long-term dataset with multiple sensors for autonomous driving)标准数据集及实车试验平台的验证结果表明:SAU-LIO算法在保证实时性的前提下,与对比算法相比具有更高的定位精度,在低速工况下,平均定位误差较次优的对比算法减小14.3%,在组合工况下,平均定位误差较次优的对比算法减小9.4%。
基金The MKE(The Ministry of Knowledge Economy),Korea,under the ITRC(Infor mation Technology Research Center)support programsupervised by the NIPA(National ITIndustry Promotion Agency)(NIPA-2012-C1090-1221-0010)TheMKE,Korea,under the Human Resources Development Programfor Convergence Robot Specialists support programsu-pervised by the NIPA(NIPA-2012-H1502-12-1002)Basic Science Research Program through the NRF funded by the MEST(2011-0025980)and MEST(2012-0005487)
文摘Odometry using incremental wheel encoder odometry suffers from the accumulation of kinematic sensors provides the relative robot pose estimation. However, the modeling errors of wheels as the robot＇s travel distance increases. Therefore, the systematic errors need to be calibrated. The University of Michigan Benchmark（UMBmark） method is a widely used calibration scheme of the systematic errors in two wheel differential mobile robots. In this paper, the accurate parameter estimation of systematic errors is proposed by extending the conventional method. The contributions of this paper can be summarized as two issues. The first contribution is to present new calibration equations that reduce the systematic odometry errors. The new equations were derived to overcome the limitation of conventional schemes. The second contribu tion is to propose the design guideline of the test track for calibration experiments. The calibration performance can be im proved by appropriate design of the test track. The simulations and experimental results show that the accurate parameter es timation can be implemented by the proposed method.
