Category Archives: Mobile Robot Localization

Interesting improvements in MC localization

Alireza Mohseni, Vincent Duchaine, Tony Wong, Improvement in Monte Carlo localization using information theory and statistical approaches, Engineering Applications of Artificial Intelligence, Volume 131, 2024 DOI: 10.1016/j.engappai.2024.107897.

Monte Carlo localization methods deploy a particle filter to resolve a hidden Markov process based on recursive Bayesian estimation, which approximates the internal states of a dynamic system given observation data. When the observed data are corrupted by outliers, the particle filter’s performance may deteriorate, preventing the algorithm from accurately computing dynamic system states such as a robot’s position, which in turn reduces the accuracy of the localization and navigation. In this paper, the notion of information entropy is used to identify outliers. Then, a probability-based approach is used to remove the discovered outliers. In addition, a new mutation process is added to the localization algorithm to exploit the posterior probability density function in order to actively detect the high-likelihood region. The goal of incorporating the mutation operator into this method is to solve the problem of algorithm impoverishment which is due to insufficient representation of the complete probability density function. Simulation experiments are used to confirm the effectiveness of the proposed techniques. They also are employed to predict the remaining viability of a lithium-ion battery. Furthermore, in an experimental study, the modified Monte Carlo localization algorithm was applied to a mobile robot to demonstrate the local planner’s improved accuracy. The test results indicate that developed techniques are capable of effectively capturing the dynamic behavior of a system and accurately tracking its characteristics.

Improving EKF and UKF when diverse precision sensors are used for localization through adaptive covariances

Giseo Park, Optimal vehicle position estimation using adaptive unscented Kalman filter based on sensor fusion, Mechatronics, Volume 99, 2024 DOI: 10.1016/j.mechatronics.2024.103144.

Precise position recognition systems are actively used in various automotive technology fields such as autonomous vehicles, intelligent transportation systems, and vehicle driving safety systems. In line with this demand, this paper proposes a new vehicle position estimation algorithm based on sensor fusion between low-cost standalone global positioning system (GPS) and inertial measurement unit (IMU) sensors. In order to estimate accurate vehicle position information using two complementary sensor types, adaptive unscented Kalman filter (AUKF), an optimal state estimation algorithm, is applied to the vehicle kinematic model. Since this AUKF includes an adaptive covariance matrix whose value changes under GPS outage conditions, it has high estimation robustness even if the accuracy of the GPS measurement signal is low. Through comparison of estimation errors with both extended Kalman filter (EKF) and UKF, which are widely used state estimation algorithms, it can be confirmed how improved the estimation performance of the proposed AUKF algorithm in real-vehicle experiments is. The given test course includes roads of various shapes as well as GPS outage sections, so it is suitable for evaluating vehicle position estimation performance.

Correcting systematic and non-systematic errors in odometry

Bibiana Fari�a, Jonay Toledo, Leopoldo Acosta, Improving odometric sensor performance by real-time error processing and variable covariance, Mechatronics, Volume 98, 2024 DOI: 10.1016/j.mechatronics.2023.103123.

This paper presents a new method to increase odometric sensor accuracy by systematic and non-systematic errors processing. Mobile robot localization is improved combining this technique with a filter that fuses the information from several sensors characterized by their covariance. The process focuses on calculating the odometric speed difference with respect to the filter to implement an error type detection module in real time. The correction of systematic errors consists in an online parameter adjustment using the previous information and conditioned by the filter accuracy. This data is also applied to design a variable odometric covariance which describes the sensor reliability and determines the influence of both errors on the robot localization. The method is implemented in a low-cost autonomous wheelchair with a LIDAR, IMU and encoders fused by an UKF algorithm. The experimental results prove that the estimated poses are closer to the real ones than using other well-known previous methods.

Using results from belief-based planning for Bayesian inference in robotics

Farhi, E.I., Indelman, V., Bayesian incremental inference update by re-using calculations from belief space planning: a new paradigm, Auton Robot 46, 783\u2013816 (2022). DOI: 10.1007/s10514-022-10045-w.

Inference and decision making under uncertainty are key processes in every autonomous system and numerous robotic problems. In recent years, the similarities between inference and decision making triggered much work, from developing unified computational frameworks to pondering about the duality between the two. In spite of these efforts, inference and control, as well as inference and belief space planning (BSP) are still treated as two separate processes. In this paper we propose a paradigm shift, a novel approach which deviates from conventional Bayesian inference and utilizes the similarities between inference and BSP. We make the key observation that inference can be efficiently updated using predictions made during the decision making stage, even in light of inconsistent data association between the two. We developed a two staged process that implements our novel approach and updates inference using calculations from the precursory planning phase. Using autonomous navigation in an unknown environment along with iSAM2 efficient methodologies as a test case, we benchmarked our novel approach against standard Bayesian inference, both with synthetic and real-world data (KITTI dataset). Results indicate that not only our approach improves running time by at least a factor of two while providing the same estimation accuracy, but it also alleviates the computational burden of state dimensionality and loop closures.

Real-time and Bayesian-enabled ICP for mobile robot localization and mapping in a Bayesian framework

Maken FA, Ramos F, Ott L. , Bayesian iterative closest point for mobile robot localization, The International Journal of Robotics Research. 2022;41(9-10):851-874 DOI: 10.1177/02783649221101417.

Accurate localization of a robot in a known environment is a fundamental capability for successfully performing path planning, manipulation, and grasping tasks. Particle filters, also known as Monte Carlo localization (MCL), are a commonly used method to determine the robot\u2019s pose within its environment. For ground robots, noisy wheel odometry readings are typically used as a motion model to predict the vehicle\u2019s location. Such a motion model requires tuning of various parameters based on terrain and robot type. However, such an ego-motion estimation is not always available for all platforms. Scan matching using the iterative closest point (ICP) algorithm is a popular alternative approach, providing ego-motion estimates for localization. Iterative closest point computes a point estimate of the transformation between two poses given point clouds captured at these locations. Being a point estimate method, ICP does not deal with the uncertainties in the scan alignment process, which may arise due to sensor noise, partial overlap, or the existence of multiple solutions. Another challenge for ICP is the high computational cost required to align two large point clouds, limiting its applicability to less dynamic problems. In this paper, we address these challenges by leveraging recent advances in probabilistic inference. Specifically, we first address the run-time issue and propose SGD-ICP, which employs stochastic gradient descent (SGD) to solve the optimization problem of ICP. Next, we leverage SGD-ICP to obtain a distribution over transformations and propose a Markov Chain Monte Carlo method using stochastic gradient Langevin dynamics (SGLD) updates. Our ICP variant, termed Bayesian-ICP, is a full Bayesian solution to the problem. To demonstrate the benefits of Bayesian-ICP for mobile robotic applications, we propose an adaptive motion model employing Bayesian-ICP to produce proposal distributions for Monte Carlo Localization. Experiments using both Kinect and 3D LiDAR data show that our proposed SGD-ICP method achieves the same solution quality as standard ICP while being significantly more efficient. We then demonstrate empirically that Bayesian-ICP can produce accurate distributions over pose transformations and is fast enough for online applications. Finally, using Bayesian-ICP as a motion model alleviates the need to tune the motion model parameters from odometry, resulting in better-calibrated localization uncertainty.

Probabilistic ICP (Iterative Closest Point) with an intro on classical ICP

Breux Y, Mas A, Lapierre L. On-manifold probabilistic Iterative Closest Point: Application to underwater karst exploration, The International Journal of Robotics Research. 2022;41(9-10):875-902 DOI: 10.1177/02783649221101418.

This paper proposes MpIC, an on-manifold derivation of the probabilistic Iterative Correspondence (pIC) algorithm, which is a stochastic version of the original Iterative Closest Point. It is developed in the context of autonomous underwater karst exploration based on acoustic sonars. First, a derivation of pIC based on the Lie group structure of SE(3) is developed. The closed-form expression of the covariance modeling the estimated rigid transformation is also provided. In a second part, its application to 3D scan matching between acoustic sonar measurements is proposed. It is a prolongation of previous work on elevation angle estimation from wide-beam acoustic sonar. While the pIC approach proposed is intended to be a key component in a Simultaneous Localization and Mapping framework, this paper focuses on assessing its viability on a unitary basis. As ground truth data in karst aquifer are difficult to obtain, quantitative experiments are carried out on a simulated karst environment and show improvement compared to previous state-of-the-art approach. The algorithm is also evaluated on a real underwater cave dataset demonstrating its practical applicability.

See also: Maken FA, Ramos F, Ott L. Bayesian iterative closest point for mobile robot localization. The International Journal of Robotics Research. 2022;41(9-10):851-874. doi:10.1177/02783649221101417

Kalman filter with time delays

H. Zhu, J. Mi, Y. Li, K. -V. Yuen and H. Leung, VB-Kalman Based Localization for Connected Vehicles With Delayed and Lost Measurements: Theory and Experiments, EEE/ASME Transactions on Mechatronics, vol. 27, no. 3, pp. 1370-1378, June 2022 DOI: 10.1109/TMECH.2021.3095096.

Traditionally, connected vehicles (CVs) share their own sensor data that relies on the satellite with their surrounding vehicles by vehicle-to-vehicle (V2V) communication. However, the satellite-based signal sometimes may be lost due to environmental factors. Time-delays and packet dropouts may occur randomly by V2V communication. To ensure the reliability and accuracy of localization for CVs, a novel variational Bayesian (VB)-Kalman method is developed for unknown and time varying probabilities of delayed and lost measurements. In this VB-Kalman localization method, two random variables are introduced to indicate whether a measurement is delayed and available, respectively. A hierarchical model is then formulated and its parameters and state are simultaneously estimated by the VB technique. Experimental results validate the proposed method for the localization of CVs in practice.

NOTE: See also S. Guo, Y. Liu, Y. Zheng and T. Ersal, “A Delay Compensation Framework for Connected Testbeds,” in IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, no. 7, pp. 4163-4176, July 2022, doi: 10.1109/TSMC.2021.3091974.

Localizing robots within pipes through RF signals

Carlos Rizzo, Teresa Seco, Jesús Espelosín, Francisco Lera, José Luis Villarroel, An alternative approach for robot localization inside pipes using RF spatial fadings, . Robotics and Autonomous Systems, Volume 136, 2021 DOI: 10.1016/j.robot.2020.103702.

Accurate robot localization represents a challenge inside pipes due to the particular conditions that characterize this type of environment. Outdoor techniques (GPS in particular) do not work at all inside metal pipes, while traditional indoor localization methods based on camera or laser sensors do not perform well mainly due to a lack of external illumination and distinctive features along pipes. Moreover, humidity and slippery surfaces make wheel odometry unreliable. In this paper, we estimate the localization of a robot along a pipe with an alternative Radio Frequency (RF) approach. We first analyze wireless propagation in metallic pipes and propose a series of setups that allow us to obtain periodic RF spatial fadings (a sort of standing wave periodic pattern), together with the influence of the antenna position and orientation over these fadings. Subsequently, we propose a discrete RF odometry-like method, by means of counting the fadings while traversing them. The transversal fading analysis (number of antennas and cross-section position) makes it possible to increase the resolution of this method. Lastly, the model of the signal is used in a continuous approach serving as an RF map. The proposed localization methods outperform our previous contributions in terms of resolution, accuracy, reliability and robustness. Experimental results demonstrate the effectiveness of the RF-based strategy without the need for a previously known map of the scenario or any substantial modification of the existing infrastructure.

Fast and more exact triangulation method for robot localization using range measurements

Pınar Oğuz-Ekim, Lambiotte R., Lefebvre E., TDOA based localization and its application to the initialization of LiDAR based autonomous robots, . Robotics and Autonomous Systems, Volume 131, 2020, DOI: 10.1016/j.robot.2020.103590.

This work considers the problem of locating a single robot given a set of squared noisy range difference measurements to a set of points (anchors) whose positions are known. In the sequel, localization problem is solved in the Least-Squares (LS) sense by writing the robot position in polar/spherical coordinates. This representation transforms the original nonconvex/multimodal cost function into the quotient of two quadratic forms, whose constrained maximization is more tractable than the original problem. Simulation results indicate that the proposed method has similar accuracy to state-of-the-art optimization-based localization algorithms in its class, and the simple algorithmic structure and computational efficiency makes it appealing for applications with strong computational constraints. Additionally, location information is used to find the initial orientation of the robot with respect to the previously obtained map in scan matching. Thus, the crucial problem of the autonomous initialization and localization in robotics is solved.

Hybrid Monte Carlo + Interval-based localization

Weiss, R., Glösekötter, P., Prestes, E. et al., Hybridisation of Sequential Monte Carlo Simulation with Non-linear Bounded-error State Estimation Applied to Global Localisation of Mobile Robots, J Intell Robot Syst 99, 335–357 (2020) DOI: 10.1007/s10846-019-01118-7.

Accurate self-localisation is a fundamental ability of any mobile robot. In Monte Carlo localisation, a probability distribution over a space of possible hypotheses accommodates the inherent uncertainty in the position estimate, whereas bounded-error localisation provides a region that is guaranteed to contain the robot. However, this guarantee is accompanied by a constant probability over the confined region and therefore the information yield may not be sufficient for certain practical applications. Four hybrid localisation algorithms are proposed, combining probabilistic filtering with non-linear bounded-error state estimation based on interval analysis. A forward-backward contractor and the Set Inverter via Interval Analysis are hybridised with a bootstrap filter and an unscented particle filter, respectively. The four algorithms are applied to global localisation of an underwater robot, using simulated distance measurements to distinguishable landmarks. As opposed to previous hybrid methods found in the literature, the bounded-error state estimate is not maintained throughout the whole estimation process. Instead, it is only computed once in the beginning, when solving the wake-up robot problem, and after kidnapping of the robot, which drastically reduces the computational cost when compared to the existing algorithms. It is shown that the novel algorithms can solve the wake-up robot problem as well as the kidnapped robot problem more accurately than the two conventional probabilistic filters.