Real time egomotion of a nonholonomic vehicle using LIDAR measurements

Almeida, João; Santos, Vítor

doi:10.1002/rob.21441

Cited by 27 publications

(16 citation statements)

References 32 publications

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“…Rather than using vision information to estimate model parameters, there has also been significant work in utilizing visual information directly for motion estimation and mapping (Botterill, Mills, & Green, ; Lambert, Furgale, Barfoot, & Enright, ; Marks, Howard, Bajracharya, Cottrell, & Matthies, ). Range sensors, specifically LIDAR systems, have also been used for motion estimation in the absence of direct position measurements such as GPS (Almeida and Santos, ).…”

Section: Introductionmentioning

confidence: 99%

Model-based Prediction of Skid-steer Robot Kinematics Using Online Estimation of Track Instantaneous Centers of Rotation

2014

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This paper presents a kinematic extended Kalman filter (EKF) designed to estimate the location of track instantaneous centers of rotation (ICRs) and aid in model‐based motion prediction of skid‐steer robots. Utilizing an ICR‐based kinematic model has resulted in impressive odometry estimates for skid‐steer movement in previous works, but estimation of ICR locations was performed offline on recorded data. The EKF presented here utilizes a kinematic model of skid‐steer motion based on ICR locations. The ICR locations are learned by the filter through the inclusion of position and heading measurements. A background on ICR kinematics is presented, followed by the development of the ICR EKF. Simulation results are presented to aid in the analysis of noise and bias susceptibility. The experimental platforms and sensors are described, followed by the results of filter implementation. Extensive field testing was conducted on two skid‐steer robots, one with tracks and another with wheels. ICR odometry using learned ICR locations predicts robot position with a mean error of −0.42 m over 40.5 m of travel during one tracked vehicle test. A test consisting of driving both vehicles approximately 1,000 m shows clustering of ICR estimates for the duration of the run, suggesting that ICR locations do not vary significantly when a vehicle is operated with low dynamics.

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Section: Introductionmentioning

confidence: 99%

Model-based Prediction of Skid-steer Robot Kinematics Using Online Estimation of Track Instantaneous Centers of Rotation

2014

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“…The problem is to determine the translation vector t and the rotation matrix R that minimize the quadratic error reported in Equation (1).…”

Section: Preliminarymentioning

confidence: 99%

A Novel Genetic Scan-Matching-Based Registration Algorithm for Supporting Moving Objects Tracking Effectively and Efficiently

2021

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In this paper we describe a family of scan-matching based registration algorithms for tracking moving objects which fall in the emerging area that predicates the integration between robotics and big data applications. The scan matching approaches track paths of a mobile object by comparing maps of the environment seen by the object during its movement. Algorithms described in this paper are hybrid, i.e. they compare maps by using first a genetic pre-alignment based on a novel metrics, and then performing a finer alignment using a deterministic approach. This kind of hybridization is, indeed, not new. However, the novel metrics used in this paper leads to important new properties, namely to correct arbitrary rotational errors and to cover larger search spaces. The proposed family of algorithms is experimentally compared to other approaches, and better performance in terms of accuracy and robustness are reported. Finally, algorithms are also very fast thanks to the genetic pre-alignment task and the novel metrics we propose.

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“…The authors use Bayes filters to estimate sensor measurement uncertainty and sensor validity to intelligently choose a subset of sensors that contribute to localization accuracy. As opposed to the later publications realized in the context of SLAM, we only consider the results of the ICP algorithm as a local pose measurement, similarly to Almeida and Santos (), who use the ICP algorithm to extract the steering angle and linear velocity of a carlike vehicle to update its nonholonomic model of motion. In our approach, the 3D reconstruction of the environment is considered locally coherent, and neither loop detection nor error propagation is used.…”

Section: Related Workmentioning

confidence: 99%

“…The second approach treats the ICP output as velocity in the R ‐frame (the velocity approach ). We consider it a state‐of‐the‐art practice utilized, for example, by Almeida and Santos (). The velocity is expressed in the N ‐frame first:

{boldv}_{N, ICP} = \frac{{boldp}_{N, ICP, i} - {boldp}_{N, ICP, i - 1}}{t (i) - t (i - 1)},

where t () is time corresponding to a time‐step i .…”

Section: Multimodal Data Fusionmentioning

confidence: 99%

Robust Data Fusion of Multimodal Sensory Information for Mobile Robots

Kubelka

Oswald

Pomerleau

et al. 2014

Journal of Field Robotics

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Urban search and rescue (USAR) missions for mobile robots require reliable state estimation systems resilient to conditions given by the dynamically changing environment. We design and evaluate a data fusion system for localization of a mobile skid‐steer robot intended for USAR missions. We exploit a rich sensor suite including both proprioceptive (inertial measurement unit and tracks odometry) and exteroceptive sensors (omnidirectional camera and rotating laser rangefinder). To cope with the specificities of each sensing modality (such as significantly differing sampling frequencies), we introduce a novel fusion scheme based on an extended Kalman filter for six degree of freedom orientation and position estimation. We demonstrate the performance on field tests of more than 4.4 km driven under standard USAR conditions. Part of our datasets include ground truth positioning, indoor with a Vicon motion capture system and outdoor with a Leica theodolite tracker. The overall median accuracy of localization—achieved by combining all four modalities—was 1.2% and 1.4% of the total distance traveled for indoor and outdoor environments, respectively. To identify the true limits of the proposed data fusion, we propose and employ a novel experimental evaluation procedure based on failure case scenarios. In this way, we address the common issues such as slippage, reduced camera field of view, and limited laser rangefinder range, together with moving obstacles spoiling the metric map. We believe such a characterization of the failure cases is a first step toward identifying the behavior of state estimation under such conditions. We release all our datasets to the robotics community for possible benchmarking.

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Real time egomotion of a nonholonomic vehicle using LIDAR measurements

Cited by 27 publications

References 32 publications

Model-based Prediction of Skid-steer Robot Kinematics Using Online Estimation of Track Instantaneous Centers of Rotation

Model-based Prediction of Skid-steer Robot Kinematics Using Online Estimation of Track Instantaneous Centers of Rotation

A Novel Genetic Scan-Matching-Based Registration Algorithm for Supporting Moving Objects Tracking Effectively and Efficiently

Robust Data Fusion of Multimodal Sensory Information for Mobile Robots

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