Range-only Simultaneous Localization And Mapping Research Articles

A resilient solution to Range-Only SLAM based on a decoupled landmark range and bearing reconstruction

A Range Only Simultaneous Localization and Mapping (RO-SLAM) problem is considered in this paper. The robot is a unicycle like vehicle equipped with encoders on the actuated wheels, which measures the distance to a set of UWB landmarks located in unknown position in the surrounding. A Multi Hypotheses Extended Kalman Filter (MHEKF), one for each landmark, is designed to dynamically estimate the range and the bearing of the observed landmark. These estimates, regarded as measurements with a proper covariance matrix, are used in an EKF SLAM algorithm, endowed with a resilient module to discern and possibly to temporarily discard landmarks with an unreliable range and bearing estimate. This allows to cope with the initial uncertainty characterizing the bearing reconstruction, but also to resist the effects of outliers and to detect possible abnormal situations. Simulation and experimental results illustrate the effectiveness of the proposed approach compared to other methods available in the literature, especially in case of significant perturbations, like the sudden and unmodeled shift of the landmarks.

Efficient Distributed Particle Filter for Robust Range-Only SLAM

Compared with simultaneous localization and mapping (SLAM) problems based on Lidar or visual sensors, range-only SLAM (RO-SLAM) is lacking bearing information. It brings challenges to particle sampling and SLAM estimation. This article proposes an efficient distributed particle filter (EDPF) for RO-SLAM problems. To overcome the difficulties of sampling in a high-dimensional state space, EDPF is decomposed into a set of subparticle filters (sub-PFs) with low dimensionality. Each sub-PF corresponds with an observed beacon, which directly reduces the sampling complexity. A joint weight update method is proposed to exploit the correlation among sub-PFs. It reweighs the particles via an auxiliary distribution after each sub-PF’s local filtering and embodies a better proposal distribution for distributed filtering. In addition, a beacon diagnosis method is proposed, it can detect and reinitialize the wrong converged beacon position estimates, which further reduces the SLAM error accumulation problem. We consider a RO-SLAM system with an odometer and ultrawideband (UWB) to verify the proposed EDPF. Results show that EDPF outperforms many existing RO-SLAM methods, which obtains the best performance with the acceptable computational load.

Range-Only UWB SLAM for Indoor Robot Localization Employing Multi-Interval EFIR Rauch-Tung-Striebel Smoother

For improving the localization accuracy, a multi-interval extended finite impulse response (EFIR)-based RauchTung-Striebel (R-T-S) smoother is proposed for the range-only ultra wide band (UWB) simultaneous localization and mapping (SLAM) for robot localization. In this mode, the EFIR R-T-S (ERTS) smoother employs EFIR filter as the forward filter and the R-T-S smoothing method to smooth the EFIR filter’s output. When the east or the north position is considered as stance, the ERTS is used to smooth the position directly. Moreover, the estimation of the UWB Reference Nodes’ (RNs’) position is smoothed by the R-T-S smooth method in parallel. The test illustrates that the proposed multi-interval ERTS smoothing for range-only UWB SLAM is able to provide accurate estimation. Compared with the EFIR filter, the proposed method improves the localization accuracy by about 25.35% and 40.66% in east and north directions, respectively

Efficient Probabilistic Approach to Range-Only SLAM With a Novel Likelihood Model

The range-only simultaneous localization and mapping (RO-SLAM) problem is considered in this article. This is a difficult problem since bearing measurements are not available. Also, the frequent outliers induced by the sensor nature (typically sonar or radio pulses) render the RO-SLAM problem more challenging. The non-Gaussian distribution found in the RO-SLAM problem makes that the landmarks cannot be directly updated using the approach based on the Gaussian assumption. We propose a region-based particle filter and a proper likelihood model to realize the reduction of particle numbers and allow the introduction of new landmarks without delayed processing. A transition mode is designed from the loosely coupled way to the tightly coupled way and used to estimate the pose of the robot and locations of landmarks. Furthermore, two detection methods are, respectively, used to remove the outliers generated by the range-only sensors according to the distribution status of the landmark estimation. The proposed algorithm is evaluated through simulations and experiments with a range-only sensor, i.e., Ultrawideband (UWB). Furthermore, the obtained results are compared with the classical algorithm. The simulation and experiment results verify the validity and superiority of the proposed RO-SLAM algorithm.

An efficient approach for undelayed range-only SLAM based on Gaussian mixtures expectation

This paper deals with range-only simultaneous localization and mapping (RO-SLAM), which is of particular interest in aerial robotics where low-weight range-only devices can provide a complementary continuous estimation between robot and landmarks when using radio-based sensors. Range-only sensors work at greater distances when compared to other commonly used sensors in aerial robotics and they are low-cost. However, the spherical shell uniform distribution inherent to range-only observations poses significant technological challenges, restricting the approaches that can be used to solve this problem. This paper presents an undelayed multi-hypothesis Extended Kalman Filter (EKF) approach based on Gaussian Mixture Models (GMM) and a reduced parameterization of the state vector to improve its efficiency. The paper also proposes a new robot-to-landmark and landmark-to-landmark range-only observation model for EKF-SLAM which takes advantage of the reduced parameterization. Finally, a new scheme is proposed for updating hypothesis weights based on an independence of beacon parameters. The method is firstly validated with simulations comparing the results with other state-of-the-art methods and later validated with real experiments for 3D RO-SLAM using several radio-based range-only sensors and an aerial robot.

Range-only SLAM for robot-sensor network cooperation

This work is motivated by schemes of robot-sensor network cooperation where sensor nodes--beacons--are used as landmarks for range-only (RO) simultaneous localization and mapping (SLAM). In most existing RO-SLAM techniques beacons are considered as passive devices ignoring the capabilities they are actually endowed with. This paper proposes a RO-SLAM scheme that distributes the measurements gathering and integration between the beacons surrounding the robot. It naturally integrates inter-beacon measurements, significantly improving map and robot estimations and speeding up beacon initialization. The proposed scheme is based on sparse extended information filter (SEIF) and it is proven that it preserves the constant time and sparsity properties of SEIF and thus, inherits its efficiency and scalability. As a result, our scheme has lower robot and map estimation errors, faster beacon initialization and lower computer requirements than existing methods. This paper experimentally validates and evaluates the proposed method for 3D SLAM using an octorotor.

Robot-Beacon Distributed Range-Only SLAM for Resource-Constrained Operation.

This work deals with robot-sensor network cooperation where sensor nodes (beacons) are used as landmarks for Range-Only (RO) Simultaneous Localization and Mapping (SLAM). Most existing RO-SLAM techniques consider beacons as passive devices disregarding the sensing, computational and communication capabilities with which they are actually endowed. SLAM is a resource-demanding task. Besides the technological constraints of the robot and beacons, many applications impose further resource consumption limitations. This paper presents a scalable distributed RO-SLAM scheme for resource-constrained operation. It is capable of exploiting robot-beacon cooperation in order to improve SLAM accuracy while meeting a given resource consumption bound expressed as the maximum number of measurements that are integrated in SLAM per iteration. The proposed scheme combines a Sparse Extended Information Filter (SEIF) SLAM method, in which each beacon gathers and integrates robot-beacon and inter-beacon measurements, and a distributed information-driven measurement allocation tool that dynamically selects the measurements that are integrated in SLAM, balancing uncertainty improvement and resource consumption. The scheme adopts a robot-beacon distributed approach in which each beacon participates in the selection, gathering and integration in SLAM of robot-beacon and inter-beacon measurements, resulting in significant estimation accuracies, resource-consumption efficiency and scalability. It has been integrated in an octorotor Unmanned Aerial System (UAS) and evaluated in 3D SLAM outdoor experiments. The experimental results obtained show its performance and robustness and evidence its advantages over existing methods.

Robust Range-Only SLAM for Unmanned Aerial Systems

Range-only sensors, in contrast to bearing-only sensors or LIDAR sensors are sensors which only provides distance between two points but not bearing information. The reduced amount of information provided by this sensors makes their application in Simultaneous Localization and Mapping (SLAM) problems specially challenging due to the lack of information. The complexity of the problem becomes higher when using noisy range-only sensors like radio-based where the multi-path effect may introduce range outliers, hence reducing the precision of the SLAM solution. Furthermore, the application of Range-only SLAM to aerial systems implies a higher level of complexity due to the introduction to a 3th dimensionality in contrast to ground mobile robots where the altitude dimension is not necessary in most applications. This paper presents an efficient and robust method to the Range-only SLAM (RO-SLAM) problem when applied to aerial vehicles and radio-based range-only measurements. The method is based on a pre-filtering algorithm which is able to detect and remove range outliers and on the application of a reduced parameterization which scales better with 3D RO-SLAM problems. The method is validated with real experiments involving an aerial vehicle and multiple radio-based range-only sensors.

AUV Single Beacon Range-Only SLAM with a SOG Filter

This paper presents a solution to the Simultaneous Localization and Mapping (SLAM) problem of an AUV which uses range-only measurements towards a single beacon. It simultaneously estimates the beacon (a priori unknown) and AUV (a priori known) positions. Conventional EKF-SLAM methods cannot deal with this problem since the resultant probability density function (pdf) of the beacon-position is severely non-Gaussian during the initialization. Particle filters can do the job but, depending on the size of the space where the beacon lays, a significant number of particles may be required to populate the solution domain, making difficult to achieve real-time performance. In this paper we present a filter which represents the joint pdf of the robot and beacon positions as a Multivariate Gaussian Mixture which has the potential to represent an arbitrary pdf with a number of Gaussian 'particles' significantly smaller than the one that would be necessary if conventional particles were used instead. In our method, each particle contains a full EKF over vehicle position/velocity and the beacon position. The method is applied to locate a sub-sea panel and home to it autonomously in order to establish visual contact to later launch a visual servoing based docking task. The method is demonstrated through real-time Hardware-in-the-loop experiments (HIL) where the robot follows an observable trajectory.

WiFi SLAM algorithms: an experimental comparison

SUMMARYLocalization and mapping in indoor environments, such as airports and hospitals, are key tasks for almost every robotic platform. Some researchers suggest the use of Range-Only (RO) sensors based on WiFi (Wireless Fidelity) technology with SLAM (Simultaneous Localization And Mapping) techniques to solve both problems. The current state of the art in RO SLAM is mainly focused on the filtering approach, while the study of smoothing approaches with RO sensors is quite incomplete. This paper presents a comparison between filtering algorithms, such as EKF and FastSLAM, and a smoothing algorithm, the SAM (Smoothing And Mapping). Experimental results are obtained in indoor environments using WiFi sensors. The results demonstrate the feasibility of the smoothing approach using WiFi sensors in an indoor environment.

Range-Only SLAM With Occupancy Maps: A Set-Membership Approach

This paper proposes a new set-membership approach to solve range-only simultaneous localization and mapping (SLAM) problems in the case where the map is described by an arbitrary occupancy set (i.e., we do not assume that the map is composed of segments, punctual marks, etc.). The principle is to transform the SLAM problem into a hybrid constraint satisfaction problem (CSP), where the variables can either be real numbers, vectors, trajectories, or subsets of \BBR <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> . An extension of existing constraint propagation methods is then proposed to solve hybrid CSPs involving set-valued variables. A simulated test case is then proposed to show the feasibility of the approach.