Conference Paper

Rao-Blackwellized Particle Filtering for Mapping Dynamic Environments

Sibley Sch. of Mech. & Aerosp. Eng., Cornell Univ., Ithaca, NY
DOI: 10.1109/ROBOT.2007.364071 Conference: Robotics and Automation, 2007 IEEE International Conference on
Source: IEEE Xplore

ABSTRACT A general method for mapping dynamic environments using a Rao-Blackwellized particle filter is presented. The algorithm rigorously addresses both data association and target tracking in a single unified estimator. The algorithm relies on a Bayesian factorization to separate the posterior into: 1) a data association problem solved via particle filter; and 2) a tracking problem with known data associations solved by Kalman filters developed specifically for the ground robot environment. The algorithm is demonstrated in simulation and validated in the real world with laser range data, showing its practical applicability in simultaneously resolving data association ambiguities and tracking moving objects.

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    • "Before beginning to address anticipating the actions of other vehicles, it is clear that more reliable perception methods are required. The current state of the art of tracking from a moving vehicle was in the DUC; Team Cornell developed a formal Bayesian estimator that could track car-like obstacles reliably for tens of seconds (Miller & Campbell 2007), to less than 1 m, with less than 0.2 m s −1 accuracy. But, on occasion, a cement barrier would be mistaken for a car, or a large bush would be initialized with a strange velocity because of occlusion reasoning. "
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    • "In these works, only the observations reflected from static objects are selected and used for SLAM process, while the moving obstacles are tracked to avoid collision, independently. In [8] and [9], same problem is solved by proposing single unified framework that includes both data association and target tracking, and builds the map for stationary parts of the environment. "
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    ABSTRACT: In this paper, we present a robust RBPF-SLAM algorithm for mobile robots in non-static environments. We propose an approach for sampling particles from multiple ancestor sets, not from just one prior set. This sampling method increases the robustness of SLAM algorithm, because some particles can be updated by only observations consistent with the map, even if observation at certain time step is corrupted by environmental changes. Corrupted observations are filtered out from recursive Bayesian update process by the proposed sampling method. We also present an intermediate path estimation method to use abandoned sensor information reflected from relocated objects for map update. The map can represent the changed configuration of non-static environment by the stored sensor information and the estimated path. Results of simulations and experiments in non-static environments show the robustness of proposed RBPF-SLAM algorithm using sonar sensors.
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    • "However the static environment is assumed known. Particle filters have also been used in (Miller & Campbell, 2007) to solve the data association problem for mapping but without considering robot localization. A similar approach as in (Montemerlo et al., 2002) is used here, extended to handle unknown static maps. "
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    ABSTRACT: In this Chapter a probabilistic framework has been introduced, that enables recursive estimation of a dynamic environment model and action selection based on these uncertain estimates. The proposed approach addresses two of the main open challenges of action selection. Uncertain knowledge is expressed by probability distributions and is utilized as a basis for all decisions taken from the system. At the same time the complexity of the proposed action selection mechanism is kept lower than of most state-of-the-art algorithms. The probability distributions of all associated uncertain quantities are approximated effectively and no restrictive assumptions are made regarding their form. More specifically, a Rao-Blackwellized particle filter (RBPF) has been deployed to address the SLAM problem and conditional particle filters have been modified to be utilized with incrementally constructed maps for tracking people in the vicinity of the robot. This way a complete model of dynamic, populated environments is provided. The computational costs depend only on the required approximation accuracy and can be defined according to the requirements of the application domain. The estimated uncertain quantities are used for coordinating the behaviours of the robot so that uncertainty is kept under control and the likelihood of achieving its goals is increased. A greedy optimization algorithm is used for behaviour selection, which is computationally inexpensive. Therefore the robot can decide quickly in order to cope with its rapidly changing environment. The decisions taken may not be optimal in the sense of POMDP policies, but are always responding to the current state of the environment and are goal oriented. The goals of the system are expressed by the behaviour selection model. Results from the implementation of all proposed algorithms on the ACE robotic platform demonstrate the efficiency of the approach. The robot can decide when to pursue its given goal or when to interact with people in order to get more target information. If its uncertainty becomes large, it takes actions that improve its state estimates. It is shown that overall system uncertainty is kept low even if the robot is called to complete complex tasks. Human decision making capabilities are remarkable. Therefore, future work will focus on learning the behaviour selection model from data provided by a human expert. This way the quality of the decisions taken by the system can be improved. Formal evaluation criteria for action selection mechanisms need to be developed. This is challenging since such criteria must consider many conflicting requirements and since in almost every study different physical robots are used in variable experimental conditions. Finally, more experiments are going to be conducted in unstructured, outdoor, dynamic environments.
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