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46
Robust and Efficient Robotic Mapping
, 2008
"... Mobile robots are dependent upon a model of the environment for many of their basic functions. Locally accurate maps are critical to collision avoidance, while largescale maps (accurate both metrically and topologically) are necessary for efficient route planning. Solutions to these problems have i ..."
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Cited by 38 (12 self)
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Mobile robots are dependent upon a model of the environment for many of their basic functions. Locally accurate maps are critical to collision avoidance, while largescale maps (accurate both metrically and topologically) are necessary for efficient route planning. Solutions to these problems have immediate and important applications to autonomous vehicles, precision surveying, and domestic robots. Building accurate maps can be cast as an optimization problem: find the map that is most probable given the set of observations of the environment. However, the problem rapidly becomes difficult when dealing with large maps or large numbers of observations. Sensor noise and nonlinearities make the problem even more difficult— especially when using inexpensive (and therefore preferable) sensors. This thesis describes an optimization algorithm that can rapidly estimate the
Scalable SLAM building Conditionally Independent Local Maps
, 2007
"... Local maps algorithms have demonstrated to be well suited for mapping large environments as can reduce the computational cost and improve the consistency of the final estimation. In this paper we present a new technique that allows the use of local mapping algorithms in the context of EKF SLAM but ..."
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Cited by 19 (3 self)
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Local maps algorithms have demonstrated to be well suited for mapping large environments as can reduce the computational cost and improve the consistency of the final estimation. In this paper we present a new technique that allows the use of local mapping algorithms in the context of EKF SLAM but without the constrain of probabilistic independence between local maps. By means of this procedure, salient features of the environment or vehicle state components as velocity or global attitude, can be shared between local maps without affecting the posterior joining process or introducing any undesirable approximations in the final global map estimate. The overload cost introduced by the technique is minimum since building up local maps does not require any additional operations apart from the usual EKF steps. As the algorithm works with covariance matrices, wellknown data association techniques can be used in the usual manner. To test the technique, experimental results using a monocular camera in an outdoor environment are provided. The initialization of the features is based on the inverse depth algorithm.
Informationbased compact Pose SLAM
 IEEE Trans. Robot
, 2010
"... Abstract—Pose SLAM is the variant of simultaneous localization and map building (SLAM) is the variant of SLAM, in which only the robot trajectory is estimated and where landmarks are only used to produce relative constraints between robot poses. To reduce the computational cost of the information fi ..."
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Cited by 18 (9 self)
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Abstract—Pose SLAM is the variant of simultaneous localization and map building (SLAM) is the variant of SLAM, in which only the robot trajectory is estimated and where landmarks are only used to produce relative constraints between robot poses. To reduce the computational cost of the information filter form of Pose SLAM and, at the same time, to delay inconsistency as much as possible, we introduce an approach that takes into account only highly informative loopclosure links and nonredundant poses. This approach includes constant time procedures to compute the distance between poses, the expected information gain for each potential link, and the exact marginal covariances while moving in open loop, as well as a procedure to recover the state after a loop closure that, in practical situations, scales linearly in terms of both time and memory. Using these procedures, the robot operates most of the time in open loop, and the cost of the loop closure is amortized over long trajectories. This way, the computational bottleneck shifts to data association, which is the search over the set of previously visited poses to determine good candidates for sensor registration. To speed up data association, we introduce a method to search for neighboring poses whose complexity ranges from logarithmic in the usual case to linear in degenerate situations. The method is based on organizing the pose information in a balanced tree whose internal levels are defined using interval arithmetic. The proposed PoseSLAM approach is validated through simulations, real mapping sessions, and experiments using standard SLAM data sets. Index Terms—Information filter, information gain, interval arithmetic, Pose SLAM, state recovery, treebased data association. I.
Large scale SLAM building conditionally independent local maps: Application to monocular vision
 IEEE TRANSACTIONS ON ROBOTICS (TRO
, 2008
"... SLAM algorithms based on local maps have been demonstrated to be well suited for mapping large environments as they reduce the computational cost and improve the consistency of the final estimation. The main contribution of this paper is a novel submapping technique that does not require independen ..."
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Cited by 16 (3 self)
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SLAM algorithms based on local maps have been demonstrated to be well suited for mapping large environments as they reduce the computational cost and improve the consistency of the final estimation. The main contribution of this paper is a novel submapping technique that does not require independence between maps. The technique is based on the intrinsic structure of the SLAM problem which allows the building of submaps that can share information, remaining conditionally independent. The resulting algorithm obtains local maps in constant time during the exploration of new terrain, and recovers the global map in linear time after simple loop closures, without introducing any approximations besides the inherent EKF linearizations. The memory requirements are also linear with the size of the map. As the algorithm works in covariance form, wellknown data association techniques can be used in the usual manner. We present experimental results using a handheld monocular camera, building a map along a closed loop trajectory of 140m in a public square, with people and other clutter. Our results show that the combination of conditional independence, that enables the system to share camera and feature states between submaps, and local coordinates, that reduce the effects of linearization errors, allow us to obtain precise maps of large areas with pure monocular SLAM in real time.
Localization, mapping, and planning in 3d environments
, 2009
"... Building a map, localizing within the map, and planning using the map are fundamental problems for mobile robotics. Every mobile robotic system must incorporate some type of solution to all three problems. While the interdependency between mapping and localization is well known as the Simultaneous L ..."
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Cited by 5 (1 self)
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Building a map, localizing within the map, and planning using the map are fundamental problems for mobile robotics. Every mobile robotic system must incorporate some type of solution to all three problems. While the interdependency between mapping and localization is well known as the Simultaneous Localization and Mapping (SLAM) problem, there is a growing understanding in the research community that planning how the robot goes about mapping and exploring an environment (and operating in the environment afterwards) can avoid degenerate conditions and significantly reduce complexity of SLAM. Thus the task of exploring a new environment combines all three problems, since the robot must plan to find actions that reduce uncertainty in both mapping and localization. This combined problem is known as Active SLAM. Independently, SLAM and planning have been solved in small, two dimensional, structured domains. Robots need to move beyond these simple environments. The challenge is to develop realtime Active SLAM methods that allow robots to explore large, three dimensional, unstructured environments, and allow subsequent operation in these environments over long periods of time.
Occupancy grid rasterization in large environments for teams of robots
 in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS
, 2011
"... Abstract — We introduce a method for efficiently rasterizing large occupancy grids. Efficient Maximum Likelihood Estimation (MLE) of robot trajectories has been shown to be highly scalable using sparse SLAM algorithms such as SqrtSAM, but unfortunately such approaches don’t directly provide a raster ..."
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Cited by 4 (2 self)
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Abstract — We introduce a method for efficiently rasterizing large occupancy grids. Efficient Maximum Likelihood Estimation (MLE) of robot trajectories has been shown to be highly scalable using sparse SLAM algorithms such as SqrtSAM, but unfortunately such approaches don’t directly provide a rasterized grid map. We harness these existing SLAM methods to compute maximum likelihood (ML) robot trajectories and introduce a new efficient algorithm to rasterize a dynamic occupancy grid. We propose a spatiallyaware data structure that enables the cost of a map update to be proportional to the impact of any loop closures, resulting in better average case performance than naive methods. Furthermore, we show how redundant sensor data can be exploited to improve map quality and speed up rasterization. We evaluate our method using several data sets collected using a team of 14 autonomous robots and show success in mixed indooroutdoor urban environments as large as 220m x 170m, with 0.1m resolution. I.
The SLAM problem: a survey
"... Abstract. This paper surveys the most recent published techniques in the field of Simultaneous Localization and Mapping (SLAM). In particular it is focused on the existing techniques available to speed up the process, with the purpose to handel large scale scenarios. The main research field we plan ..."
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Cited by 4 (0 self)
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Abstract. This paper surveys the most recent published techniques in the field of Simultaneous Localization and Mapping (SLAM). In particular it is focused on the existing techniques available to speed up the process, with the purpose to handel large scale scenarios. The main research field we plan to investigate is the filtering algorithms as a way of reducing the amount of data. It seems that almost all the current approaches can not perform consistent maps for large areas, mainly due to the increase of the computational cost and due to the uncertainties that become prohibitive when the scenario becomes larger.
Temporally scalable visual SLAM using a reduced pose graph
 Computer Science and Artificial Intelligence Laboratory, MIT, Tech. Rep
, 2012
"... Abstract — In this paper, we demonstrate a system for temporally scalable visual SLAM using a reduced pose graph representation. Unlike previous visual SLAM approaches that maintain static keyframes, our approach uses new measurements to continually improve the map, yet achieves efficiency by avoidi ..."
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Cited by 4 (1 self)
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Abstract — In this paper, we demonstrate a system for temporally scalable visual SLAM using a reduced pose graph representation. Unlike previous visual SLAM approaches that maintain static keyframes, our approach uses new measurements to continually improve the map, yet achieves efficiency by avoiding adding redundant frames and not using marginalization to reduce the graph. To evaluate our approach, we present results using an online binocular visual SLAM system that uses place recognition for both robustness and multisession operation. Additionally, to enable largescale indoor mapping, our system automatically detects elevator rides based on accelerometer data. We demonstrate longterm mapping in a large multifloor building, using approximately nine hours of data collected over the course of six months. Our results illustrate the capability of our visual SLAM system to map a large area over extended period of time. I.
Iterated SLSJF: A Sparse Local Submap Joining Algorithm with Improved Consistency
"... This paper presents a new local submap joining algorithm for building largescale feature based maps. The algorithm is based on the recently developed Sparse Local Submap Joining Filter (SLSJF) and uses multiple iterations to improve the estimate and hence is called Iterated SLSJF (ISLSJF). The inp ..."
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Cited by 4 (4 self)
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This paper presents a new local submap joining algorithm for building largescale feature based maps. The algorithm is based on the recently developed Sparse Local Submap Joining Filter (SLSJF) and uses multiple iterations to improve the estimate and hence is called Iterated SLSJF (ISLSJF). The input to the ISLSJF algorithm is a sequence of local submaps. The output of the algorithm is a global map containing the global positions of all the features as well as all the robot start/end poses of the local submaps. In the submap joining step of ISLSJF, whenever the change of state estimate computed by an Extended Information Filter (EIF) is larger than a predefined threshold, the information vector and the information matrix is recomputed as a sum of all the local map contributions. This improves the accuracy of the estimate as well as avoids the possibility that the Jacobian with respect to the same feature gets evaluated at different estimate values, which is one of the major causes of inconsistency for EIF/EKF algorithms. Although the computational cost of ISLSJF is higher than that of SLSJF, the algorithm can still be implemented efficiently due to the exactly sparseness of the information matrix. The new algorithm is compared with EKF SLAM and SLSJF using both computer simulation and experimental examples. 1
A ConstantTime Algorithm for Vector Field SLAM using an Exactly Sparse Extended Information Filter
"... Abstract — Designing a localization system for a lowcost robotic consumer product poses a major challenge. In previous work, we introduced Vector Field SLAM [5], a system for simultaneously estimating robot pose and a vector field induced by stationary signal sources present in the environment. In ..."
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Cited by 4 (0 self)
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Abstract — Designing a localization system for a lowcost robotic consumer product poses a major challenge. In previous work, we introduced Vector Field SLAM [5], a system for simultaneously estimating robot pose and a vector field induced by stationary signal sources present in the environment. In this paper we show how this method can be realized on a lowcost embedded processing unit by applying the concepts of the Exactly Sparse Extended Information Filter [15]. By restricting the set of active features to the 4 nodes of the current cell, the size of the map becomes linear in the area explored by the robot while the time for updating the state can be held constant under certain approximations. We report results from running our method on an ARM 7 embedded board with 64 kByte RAM controlling a Roomba 510 vacuum cleaner in a standard test environment. NS spot1 X (sensor units) Spot1 X readings Node X1 estimates