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 Rauch-Tung-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 proposedmulti-interval ERTS smoothing for range-only UWB SLAMis 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.

Integrating WSN and Laser SLAM for Mobile Robot Indoor Localization

Localization plays a vital role in the mobile robot navigation system and is a fundamental capability for the following path planning task.In an indoor environment where the global positioning system signal fails or becomes weak,the wireless sensor network(WSN)or simultaneous localization and mapping(SLAM)scheme gradually becomes a research hot spot.WSN method uses received signal strength indicator(RSSI)values to determine the position of the target signal node,however,the orientation of the target node is not clear.Besides,the distance error is large when the indoor signal receives interference.The laser SLAM-based method usually uses a 2D laser Lidar to build an occupancy grid map,then locates the robot according to the known grid map.Unfortunately,this scheme only works effectively in those areas with salient geometrical features.The traditional particle filter always fails for areas with similar structures,such as a long corridor.To solve their shortcomings,this paper proposes a novel coarse-to-fine paradigm that uses WSN to assist mobile robot localization in a geometrically similar environment.Firstly,the fingerprints database is built in the offline stage to get reference distance information.The distance data is determined by the statistical mean value of multiple RSSI values.Secondly,a hybrid map with grid cells and RSSI values is constructed when the mobile robot moves from a starting point to the ending place.Thirdly,the RSSI values are thought of as a basic reference to get a coarse localization.Finally,an improved particle filteringmethod is presented to achieve fine localization.Experimental results demonstrate that our approach is effective and robust for global localization.The localization success rate reaches 97.0%and the average moving distance is only 0.74 meters,while the traditional method always fails.In addition,the method also works well when the mobile robot is kidnapped to another position in the environment.

Improved Rao-Blackwellized particle filter for simultaneous robot localization and person-tracking with single mobile sensor

A probabilistic algorithm is proposed for the problem of simultaneous robot localization and peopletracking （SLAP） using single onboard sensor in situations with sensor noise and global uncertainties over the observer＇s pose. By the decomposition of the joint distribution according to the Rao-Blackwell theorem, posteriors of the robot pose are sequentially estimated over time by a smoothed laser perception model and an improved resampling scheme with evolution strategies; the conditional distribution of the person＇s position is estimated using unscented Kalman filter （UKF） to deal with the nonlinear dynamic of human motion. Experiments conducted in a real indoor service robot scenario validate the favorable performance of the positional accuracy as well as the improved computational efficiency.

Poles Extraction Based LiDAR Localization for Autonomous Robot Systems

With the development of intelligent technology,the problem of Li DAR-based localization has played an increasingly important role in the localization of robots in unmanned systems.Since the dense point clouds from the environment requires a large amount of memory,specific and stable structural features,such as pole-like objects in the environment,can serve as the ideal landmarks for localization in unmanned systems,which can effectively reduce the memory usage and mitigate the effects of dynamic environment changes.In this paper,the authors propose a pole-like objects extraction approach and then,it is applied into the localization system of mobile robots.Under the same experimental conditions,the proposed method can extract more pole-like objects and achieve better long-term localization accuracy than some existing methods in different urban environmental datasets.

Place recognition based on saliency for topological localization

Based on salient visual regions for mobile robot navigation in unknown environments, a new place recognition system was presented. The system uses monocular camera to acquire omni-directional images of the environment where the robot locates. Salient local regions are detected from these images using center-surround difference method, which computes opponencies of color and texture among multi-scale image spaces. And then they are organized using hidden Markov model (HMM) to form the vertex of topological map. So localization, that is place recognition in our system, can be converted to evaluation of HMM. Experimental results show that the saliency detection is immune to the changes of scale, 2D rotation and viewpoint etc. The created topological map has smaller size and a higher ratio of recognition is obtained.

Robot SLAM with Ad hoc wireless network adapted to search and rescue environments

An innovative multi-robot simultaneous localization and mapping(SLAM)is proposed based on a mobile Ad hoc local wireless sensor network(Ad-WSN).Multiple followed-robots equipped with the wireless link RS232/485module act as mobile nodes,with various on-board sensors,Tp-link wireless local area network cards,and Tp-link wireless routers.The master robot with embedded industrial PC and a complete robot control system autonomously performs the SLAM task by exchanging information with multiple followed-robots by using this self-organizing mobile wireless network.The PC on the remote console can monitor multi-robot SLAM on-site and provide direct motion control of the robots.This mobile Ad-WSN complements an environment devoid of usual GPS signals for the robots performing SLAM task in search and rescue environments.In post-disaster areas,the network is usually absent or variable and the site scene is cluttered with obstacles.To adapt to such harsh situations,the proposed self-organizing mobile Ad-WSN enables robots to complete the SLAM process while improving the performances of object of interest identification and exploration area coverage.The information of localization and mapping can communicate freely among multiple robots and remote PC control center via this mobile Ad-WSN.Therefore,the autonomous master robot runs SLAM algorithms while exchanging information with multiple followed-robots and with the remote PC control center via this local WSN environment.Simulations and experiments validate the improved performances of the exploration area coverage,object marked,and loop closure,which are adapted to search and rescue post-disaster cluttered environments.

Coordinated target localization base on pseudo measurement for clustered space robot

This paper presents a coordinated target localization method for clustered space robot.According to the different measuring capabilities of cluster members,the master-slave coordinated relative navigation strategy for target localization with respect to slavery space robots is proposed;then the basic mathematical models,including coordinated relative measurement model and cluster centralized dynamics,are established respectively.By employing the linear Kalman flter theorem,the centralized estimator based on truth measurements is developed and analyzed frstly,and with an intention to inhabit the initial uncertainties related to target localization,the globally stabilized estimator is designed through introduction of pseudo measurements.Furthermore,the observability and controllability of stochastic system are also analyzed to qualitatively evaluate the convergence performance of pseudo measurement estimator.Finally,on-orbit target approaching scenario is simulated by using semi-physical simulation system,which is used to verify the convergence performance of proposed estimator.During the simulation,both the known and unknown maneuvering acceleration cases are considered to demonstrate the robustness of coordinated localization strategy.

Preliminary laboratory test on navigation accuracy of an autonomous robot for measuring air quality in livestock buildings

Air quality in many poultry buildings is less than desirable.However,the measurement of concentrations of airborne pollutants in livestock buildings is generally quite difficult.To counter this,the development of an autonomous robot that could collect key environmental data continuously in livestock buildings was initiated.This research presents a specific part of the larger study that focused on the preliminary laboratory test for evaluating the navigation precision of the robot being developed under the different ground surface conditions and different localization algorithm according internal sensors.The construction of the robot was such that each wheel of the robot was driven by an independent DC motor with four odometers fixed on each motor.The inertial measurement unit(IMU)was rigidly fixed on the robot vehicle platform.The research focused on using the internal sensors to calculate the robot position(x,y,θ)through three different methods.The first method relied only on odometer dead reckoning(ODR),the second method was the combination of odometer and gyroscope data dead reckoning(OGDR)and the last method was based on Kalman filter data fusion algorithm(KFDF).A series of tests were completed to generate the robot’s trajectory and analyse the localisation accuracy.These tests were conducted on different types of surfaces and path profiles.The results proved that the ODR calculation of the position of the robot is inaccurate due to the cumulative errors and the large deviation of the heading angle estimate.However,improved use of the gyroscope data of the IMU sensor improved the accuracy of the robot heading angle estimate.The KFDF calculation resulted in a better heading angle estimate than the ODR or OGDR calculations.The ground type was also found to be an influencing factor of localisation errors.