Toward Deployment of Large Scale Simultaneous Localisation and Map Building (SLAM) Systems

Simultaneous Localization and Mapping is widespread in both robotics and autonomous driving. This paper proposes a novel method to identify changes in maps constructed by SLAM algorithms without feature-to-feature comparison. We use ICP-like algorithms to match frames and pose graph optimization to solve the SLAM problem. Finally, we analyze the residuals to localize possible alterations of the map. The concept was tested with 2D LIDAR SLAM problems in simulated and real-life cases.

Reliable localisation is an essential component of any autonomous vehicle system. The basic navigation loop is based on dead reckoning sensors that predict high frequency vehicle manoeuvres and low frequency absolute sensors that bound positioning errors. The problem of localisation given a map of the environment or estimating the map knowing the vehicle position has been addressed and solved using a number of different approaches. A related problem is when neither, the map nor the vehicle position is known. In this case the vehicle, with known kinematics, starts in an unknown location in an unknown environment and proceeds to incrementally build a navigation map of the environment while simultaneously using this map to update its location. In this problem, vehicle and map estimates are highly correlated and cannot be obtained independently of one another. This problem is usually known as Simultaneous Localisation and Map Building (SLAM). As an incremental algorithm, the SLAM in large outdoor environments must address several particular problems: the perception of the environment and the nature of features searched or observables with the available sensors, the number of features needed to successfully localise, the type of representation used for the features, a real time management of the map and the fusion algorithm, the consistency of the SLAM process and the data association between features mapped and observations. A good insight into the SLAM problem can be found in Durrant-Whyte & Bailey (2006). This chapter presents recent contributions in the areas of perception, representation and data fusion, focusing on solutions that address the real time problem in large outdoor environments. Topics such as DenseSLAM, Robust Navigation and non-Gaussian Observations in SLAM are summarised and illustrated with real outdoor tests.

Current substation inspection robots mainly use Lidar as a sensor for localization and map building. However, laser SLAM has the problem of localization error in scenes with similar and missing environmental structural features, and environmental maps built by laser SLAM provide more single-road information for inspection robot navigation, which is not conducive to the judgment of the road scene. For this reason, in this paper, 3D Lidar information and visual information are fused to create a SLAM algorithm applicable to substation inspection robots to solve the above laser SLAM localization error problem and improve the algorithm’s localization accuracy. First, in order to recover the scalability of monocular visual localization, the algorithm in this paper utilizes 3D Lidar information and visual information to calculate the true position of image feature points in space. Second, the laser position and visual position are utilized with interpolation to correct the point cloud distortion caused by the motion of the Lidar. Then, a position-adaptive selection algorithm is designed to use visual position instead of laser inter-frame position in some special regions to improve the robustness of the algorithm. Finally, a color laser point cloud map of the substation is constructed to provide more road environment information for the navigation of the inspection robot. The experimental results show that the localization accuracy and map-building effect of the VO-Lidar SLAM algorithm designed in this paper are better than the current laser SLAM algorithm and verify the applicability of the color laser point cloud map constructed by this algorithm in substation environments.

Simultaneous localization and mapping is a stochastic map building method which permits consistent robot navigation without requiring an a priori map. The map is built incrementally as the robot observes the environment with its on-board sensors and, at the same time, is used to localise the robot. In this paper simultaneous localization and map building is performed with a metric approach which permits both precision and robustness. The most important innovation of the approach is the way how errors in the control of robot localisation are handled by map building using the information of the landmarks localization. The method uses data from a laser scanner to extract distances and orientations of landmark and combines control localization and metric paradigm. The metric approach, based on the Kalman Filter, uses a new concept to avoid the problem of the drift in odometry. The simulation section will validate the maps representation approach and presents different aspect of environments.

This paper develops a simultaneous localisation and map building (SLAM) algorithm which utilises the split covariance intersection (SCI) update rule. This algorithm decomposes estimates into a correlated component (whose precise structure is unknown) and a lower bound on an independent component. For a map of n beacons, the storage is O(n) and the computational costs are constant irrespective of map size. In a simple simulation example we show that the SCI algorithm, through exploiting a lower bound on independent information, performs substantially better than the traditional covariance intersection SLAM algorithm.

This paper represents a SLAM(simultaneous localization and map building) system with odometry and indoor GPS sensor. This paper presents an accurate localization method by relative and absolute sensor fusion for map building system of mobile robot, we developed a sensor based absolution position tracking algorithm and grid based map building algorithm using this algorithm. A wall following decision engine like a fuzzy algorithm was proposed for map building navigation. Generally, while the robot is navigating the environment for map building position is estimated by odometry. However the position estimation using encoder data is not accurate owing to slipping and odometry linearization error. To solve this problem we proposed a sensor fusion method using encoder and indoor GPS. We proved this system' s validity through field test.

This thesis presents a recent research on the problem of environmental modeling for both localization and map building for wheel-based, differential driven, fully autonomous and self-contained mobile robots. The robots behave in an indoor office environment. They have a multi-sensor setup where the encoders are used for odometry and two exteroperceptive sensors, a 360° laser scanner and a monocular vision system, are employed to perceive the surrounding. The whole approach is feature based meaning that instead of directly using the raw data from the sensor features are firstly extracted. This allows the filtering of noise from the sensors and permits taking account of the dynamics in the environment. Furthermore, a properly chosen feature extraction has the characteristic of better isolating informative patterns. When describing these features care has to be taken that the uncertainty from the measurements is taken into account. The representation of the environment is crucial for mobile robot navigation. The model defines which perception capabilities are required and also which navigation technique is allowed to be used. The presented environmental model is both metric and topological. By coherently combining the two paradigms the advantages of both methods are added in order to face the drawbacks of a single approach. The capabilities of the hybrid approach are exploited to model an indoor office environment where metric information is used locally in structures (rooms, offices), which are naturally defined by the environment itself while the topology of the whole environment is resumed separately thus avoiding the need of global metric consistency. The hybrid model permits the use of two different and complementary approaches for localization, map building and planning. This combination permits the grouping of all the characteristics which enables the following goals to be met: Precision, robustness and practicability. Metric approaches are, per definition, precise. The use of an Extended Kalman Filter (EKF) permits to have a precision which is just bounded by the quality of the sensor data. Topological approaches can easily handle large environments because they do not heavily rely on dead reckoning. Global consistency can, therefore, be maintained for large environments. Consistent mapping, which handle large environments, is achieved by choosing a topological localization approach, based on a Partially Observable Markov Decision Process (POMDP), which is extended to simultaneous localization and map building. The theory can be mathematically proven by making some assumptions. However, as stated during the whole work, at the end the robot itself has to show how good the theory is when used in the real world. For this extensive experimentation for a total of more than 9 km is performed with fully autonomous self-contained robots. These experiments are then carefully analyzed. With the metric approach precision with error bounds of about 1 cm and less than 1 degree is further confirmed by ground truth measurements with a mean error of less than 1 cm. The topological approach is successfully tested by simultaneous localization and map building where the automatically created maps turned out to work better than the a priori maps. Relocation and closing the loop are also successfully tested.

For autonomous navigation in workspace, a mobile robot has to be able to know its position in this space in a precise way that means that the robot must be able to self-localize to move and perform successfully the different entrusted tasks. At present, one of the most used systems in open spaces is the GPS navigation system; however, in indoor spaces (factories, buildings, hospitals, warehouses...) GPS signals are not operative because their intensity is too weak. The absence of GPS navigation systems in these environments has stimulated the development of new local positioning systems with their particular problems. Such systems have required in many cases the installation of beacons that operate like satellites (similar to GPS), the use of landmarks or even the use of other auxiliary systems to determine the robot’s position. The problem of mobile robot localization is a part of a more global problem because in autonomous navigation when a robot is exploring an unknown environment, it usually needs to obtain some important information: a map of the environment and the robot’s location in the map. Since mapping and localization are related to each other, these two problems are usually considered as a single problem called simultaneous localization and mapping (SLAM). The problem of Simultaneous Localization and Map Building is a significant open problem in mobile robotics which is difficult because of the following paradox: to localize itself the robot needs the map of the environment, and, for building a map the robot location must be known precisely. Mobile robots use different kinds of sensors to determine their position: for instance it is very common the use of odometric or inertial sensors, however it is remarkable to consider that in wheel slippage, sensor drifts a noise causing error accumulation, thus leading to erroneous estimates. Another kind of external sensors used in robotics in order to solve localization are for instance CCD cameras, infrared sensor, ultra sonic sensor, mechanical wave and laser. Other sensors recently applied are the instruments sensible to the magnetic field known as the electronic compass (Navarro & Benet, 2009). Mobile robotics are interested on those able to measure the Earths magnetic field and express it through an electrical signal. One type of electronic compass is based on magneto-resistive transducers, whose electrical resistance varies with the changes on the applied magnetic field. This type of sensors presents sensitivities below 0.1 milligauss, with response times below 1 sec, allowing its reliable use in vehicles moving at high speeds (Caruso, 2000). In SLAM some applications with electronic compass have been developed working simultaneously with other sensors such as artificial vision (Kim et al., 2006) and ultrasonic sensors (Kim et al., 2007).

This paper describes a scalable algorithm for the simultaneous mapping and localization (SLAM) problem. SLAM is the problem of determining the location of environmental features with a roving robot. Many of today’s popular techniques are based on extended Kalman filters (EKFs), which require update time quadratic in the number of features in the map. This paper develops the notion of sparse extended information filters (SEIFs), as a new method for solving the SLAM problem. SEIFs exploit structure inherent in the SLAM problem, representing maps through local, Web-like networks of features. By doing so, updates can be performed in constant time, irrespective of the number of features in the map. This paper presents several original constant-time results of SEIFs, and provides simulation results that show the high accuracy of the resulting maps in comparison to the computationally more cumbersome EKF solution.

Urban large-scale digital mapping demanded a greatly efficient and speedy surveying method. The traditional surveying process lagged behind because it had many shortcomings in mapping cost, mapping period, data storage and further usage. It is an effective way to use high spatial resolution remote sensing image to make large-scale urban maps. Firstly this paper discussed the relationship between mapping scale and image spatial resolution, analyzed the way of processing image about large-scale mapping, explained the data-abstracted method based on customized rules, intelligent symbols and automated topology. Secondly this paper expatiated on the core technology of geo-database based on large-scale digital maps and high spatial resolution image, argued how to establish and maintain a spatial database and how to serve the other different applications by the public interface. The remote sensing image, large-scale digital maps, intelligent symbols, map elements, map-framing indexes and social statistics data were stored into the same spatial database which further provided comprehensive data and the thematic maps for urban administration, urban plan, land management, digital city and Urban GIS. And then the spatio-temporal geo-database was established by the "versioning" which could manage different spatial and temporal data. So it was feasible to produce large-scale digital maps using high spatial resolution remote sensing image and construct spatio-temporal database using "versioning." Furthermore it is a necessary way to urban informational and intelligent administration.

The imprecision of sensor measurements due to systematic and nonsystematic errors can give rise to severe problems in several autonomous navigation tasks. In particular, the errors due to sensor bias can render existing Simultaneous Localization and Map Building (SLAM algorithms useless as such biases cause the estimators to diverge. This paper describes a method to estimate and compensate these ever present and particularly cumbersome sensor and input biases in real time in the context of SLAM applications without an a priori map of the environment. The validity of the proposed methodology is verified via simulations for the case of an autonomous land vehicle navigating in a completely unknown 20 terrain. It is assumed that biased and noisy range and bearing measurements to point landmarks are obtainable in real-time, using a sensor such as a laser scanner.

Simultaneous Localization and Mapping or SLAM, is a family of algorithms that solve the problem of estimating an observer's position in an unknown environment while generating a map of that environment. SLAM algorithms that produce high quality dense maps require powerful hardware platforms. In the simultaneous solution of these two problems, Localization, also known as Tracking, is the one that is latency sensitive and needs a sustained high framerate. This work focuses on providing an efficient, high-performance solution for Direct Tracking using a high bandwidth streaming architecture, optimized for maximum memory throughput. At its centre is a Tracking Core that performs non-linear least-squares optimization for direct whole-image alignment. The architecture is designed to scale with the available hardware resources in order to enable its use for different performance/cost levels and platforms. An initial implementation tested with a Zynq System-on-Chip can process and track more than 22 frames/second with an embedded power budget and achieves a 5 χ improvement over previous work on FPGA SoCs.

In this paper, we propose a vision-based simultaneous localization and map-building (SLAM) algorithm using compressed extended Kalman filter (CEKF). SLAM addresses the problem of locating a mobile robot in unknown environments. Extended Kalman filters (EKF) are widely used to solve this problem. However, this filter is very time consuming. To reduce the computational complexity, we apply a CEKF to stereo images while compensating for some of the limitation shown in previous implementations of CEKF. Moreover, we estimate the full DOF, its position and pose, of the mobile robots which is required when operating in the outdoor environment. Outdoor experiments have been conducted to test the effectiveness of the proposed SLAM algorithm.

Simultaneous Localization and Mapping SLAM is one of the most important capabilities for autonomous mobile robots, and many researches have been proposed demonstrating the effective SLAM methods. However, these SLAM methods sometimes require assumptions such as the sensor model, which is difficult to implement and use the SLAM methods. In our previous work, a SLAM method based on Evolution Strategy ES was proposed and the on-line SLAM in indoor environments was realized. However, the definition of the map building method was not clear. Therefore, we propose a SLAM method based on a simple map building and search method. In this paper, we explain our autonomous mobile robot system and propose our SLAM method based on µ+1-ES. The experimental results show the effectiveness of the proposed method.

Addresses the problem of implementing simultaneous localisation and map building (SLAM) in very large outdoor environments. A method is presented to reduce the computational requirement from /spl sim/O(N/sup 2/) to /spl sim/O(N), N being the states used to represent all the landmarks and vehicle pose. With this implementation the memory requirements are also reduced to /spl sim/O(N). This algorithm presents an efficient solution to the full update required by the compressed extended Kalman filter algorithm. Experimental results are also presented.