Benson Limketkai

Distributed Multirobot Exploration and Mapping

Efficient exploration of unknown environments is a fundamental problem in mobile robotics. We present an approach to distributed multirobot mapping and exploration. Our system enables teams of robots to efficiently explore environments from different, unknown locations. In order to ensure consistency when combining their data into shared maps, the robots actively seek to verify their relative locations. Using shared maps, they coordinate their exploration strategies to maximize the efficiency of exploration. This system was evaluated under extremely realistic real-world conditions. An outside evaluation team found the system to be highly efficient and robust. The maps generated by our approach are consistently more accurate than those generated by manually measuring the locations and extensions of rooms and objects

A practical, decision-theoretic approach to multi-robot mapping and exploration

An important assumption underlying virtually all approaches to multi-robot exploration is prior knowledge about their relative locations. This is due to the fact that robots need to merge their maps so as to coordinate their exploration strategies. The key step in map merging is to estimate the relative locations of the individual robots. This paper presents a novel approach to multi-robot map merging under global uncertainty about the robots relative locations. Our approach uses an adapted version of particle filters to estimate the position of one robot in the other robots partial map. The risk of false-positive map matches is avoided by verifying match hypotheses using a rendezvous approach. We show how to seamlessly integrate this approach into a decision-theoretic multi-robot coordination strategy. The experiments show that our sample-based technique can reliably find good hypotheses for map matches. Furthermore, we present results obtained with two robots successfully merging their maps using the decision-theoretic rendezvous strategy.

Efficient Sparse Pose Adjustment for 2D mapping

Pose graphs have become a popular representation for solving the simultaneous localization and mapping (SLAM) problem. A pose graph is a set of robot poses connected by nonlinear constraints obtained from observations of features common to nearby poses. Optimizing large pose graphs has been a bottleneck for mobile robots, since the computation time of direct nonlinear optimization can grow cubically with the size of the graph. In this paper, we propose an efficient method for constructing and solving the linear subproblem, which is the bottleneck of these direct methods. We compare our method, called Sparse Pose Adjustment (SPA), with competing indirect methods, and show that it outperforms them in terms of convergence speed and accuracy. We demonstrate its effectiveness on a large set of indoor real-world maps, and a very large simulated dataset. Open-source implementations in C++, and the datasets, are publicly available.

Map merging for distributed robot navigation

A set of robots mapping an area can potentially combine their information to produce a distributed map more efficiently than a single robot alone. We describe a general framework for distributed map building in the presence of uncertain communication. Within this framework, we then present a technical solution to the key decision problem of determining relative location within partial maps.

Centibots: very large scale distributed robotic teams

We describe the development of Centibots, a framework for very large teams of robots that are able to perceive, explore, plan and collaborate in unknown environments.

Towards object mapping in non-stationary environments with mobile robots

We propose an occupancy grid mapping algorithm for mobile robots operating in environments where objects change their locations over time. Our approach uses a straightforward map differencing technique to detect changes in an environment over time. It employs the expectation maximization algorithm to learn models of non-stationary objects, and to determine the location of such objects in individual occupancy grid maps built at different points in time. By combining data from multiple maps when learning object models, the resulting models have higher fidelity than could be obtained from any single map. A Bayesian complexity measure is applied to determine the number of different objects in the model, making it possible to apply the approach to situations where not all objects are present at all times in the map.

Distributed multirobot exploration, mapping, and task allocation

We present an integrated approach to multirobot exploration, mapping and searching suitable for large teams of robots operating in unknown areas lacking an existing supporting communications infrastructure. We present a set of algorithms that have been both implemented and experimentally verified on teams—of what we refer to as Centibots—consisting of as many as 100 robots. The results that we present involve search tasks that can be divided into a mapping stage in which robots must jointly explore a large unknown area with the goal of generating a consistent map from the fragment, a search stage in which robots are deployed within the environment in order to systematically search for an object of interest, and a protection phase in which robots are distributed to track any intruders in the search area. During the first stage, the robots actively seek to verify their relative locations in order to ensure consistency when combining data into shared maps; they must also coordinate their exploration strategies so as to maximize the efficiency of exploration. In the second and third stages, robots allocate search tasks among themselves; since tasks are not defined a priori, the robots first produce a topological graph of the area of interest and then generate a set of tasks that reflect spatial and communication constraints. Our system was evaluated under extremely realistic real-world conditions. An outside evaluation team found the system to be highly efficient and robust.

Comparison of indoor robot localization techniques in the absence of GPS

When available, GPS is the quick and easy solution to localizing a robot. However, because it is often not available (e.g. indoors) or not reliable enough, other techniques, using laser range finders or cameras have been developed that offer better performance. For 2D localization,lLaser range finders are far more precise and easier to work with than cameras. We report here on the performance of several implementations of the main class of localization algorithms that use a laser, Simultaneous Localization And Mapping (SLAM) on the RAWSEEDS benchmark. SRI Internationals SLAM system has an RMS error in XY of 0.32m (0.22%). This is the best reported performance on this benchmark.

Learning Occupancy Grids of Non-Stationary Objects with Mobile Robots

We propose an occupancy grid mapping algorithm for mobile robots operating in environments where objects may change their locations over time. Most mapping algorithms rely on a static world assumption, which cannot model non-stationary objects (chairs, desks,...). This paper describes an extension to the well-known occupancy grid mapping technique [5,10] for learning models of non-stationary objects. Our approach uses a map differencing technique to extract snapshots of non-stationary objects. It then employs the expectation maximization (EM) algorithm to learn models of these objects, and to solve the data association problem that arises when objects are seen at different places at different points in time. A Bayesian version of Occam’s razor is applied to determine the number of different objects in the model. Experimental results obtained in two different indoor environments illustrate that our approach robustly solves the data association problem, and generates accurate models of environments with non-stationary objects.