text: In this paper we present a novel approach to global localization using an RGB-D camera in maps of visual features. For large maps, the performance of pure image matching techniques decays in terms of robustness and computational cost. Particularly, repeated occurrences of similar features due to repeating structure in the world (e.g., doorways, chairs, etc.) or missing associations between observations pose critical challenges to visual localization. We address these challenges using a two-step approach. We first estimate a candidate pose using few correspondences between features of the current camera frame and the feature map. The initial set of correspondences is established by proximity in feature space. The initial pose estimate is used in the second step to guide spatial matching of features in 3D, i.e., searching for associations where the image features are expected to be found in the map. A RANSAC algorithm is used to compute a fine estimation of the pose from the correspondences. Our approach clearly outperforms localization based on feature matching exclusively in feature space, both in terms of estimation accuracy and robustness to failure and allows for global localization in real time (30Hz).
  @cite_15: 
  @cite_21: This paper presents a new algorithm for mobile robot localization, called Monte Carlo Localization (MCL). MCL is a version of Markov localization, a family of probabilistic approaches that have recently been applied with great practical success. However, previous approaches were either computationally cumbersome (such as grid-based approaches that represent the state space by high-resolution 3D grids), or had to resort to extremely coarse-grained resolutions. Our approach is computationally efficient while retaining the ability to represent (almost) arbitrary distributions. MCL applies sampling-based methods for approximating probability distributions, in a way that places computation "where needed." The number of samples is adapted on-line, thereby invoking large sample sets only when necessary. Empirical results illustrate that MCL yields improved accuracy while requiring an order of magnitude less computation when compared to previous approaches. It is also much easier to implement.
  @cite_16: Mobile robot localization is the problem of determining a robot’s pose from sensor data. This article presents a family of probabilistic localization algorithms known as Monte Carlo Localization (MCL). MCL algorithms represent a robot’s belief by a set of weighted hypotheses (samples), which approximate the posterior under a common Bayesian formulation of the localization problem. Building on the basic MCL algorithm, this article develops a more robust algorithm called MixtureMCL, which integrates two complimentary ways of generating samples in the estimation. To apply this algorithm to mobile robots equipped with range finders, a kernel density tree is learned that permits fast sampling. Systematic empirical results illustrate the robustness and computational efficiency of the approach.  2001 Published by Elsevier Science B.V.
summary: MCL overcomes the limitations of EKFs as mentioned earlier. It was successfully used in @cite_21 for vision-based localization of a tour-guide robot in a museum using a map of the ceiling and a camera pointing to it. In contrast to this approach, we do not rely on odometry measurements to predict the pose, and are not restricted to planar motion. Additionally, MCL would report incorrect locations after unexpected robot motions or sensor outages. Sensor Resetting Localization @cite_15 partially substitutes particles by new ones directly generated from the sensor measurements when the position estimate is uncertain. Mixture-MCL @cite_16 combines standard MCL with dual-MCL to drastically reduce computational cost and localization error. Dual-MCL also generates particles from the current sensor measurements and was shown to deal well with the kidnapped robot problem when properly combined with standard MCL. We do not need a reset process, since our estimation is independent from the prior state. Our approach could be used in combination with Monte Carlo Localization (MCL) for efficient particle initialization and weighting. However, the results of our experiments show that our estimate is accurate enough to be used as final result, without any filtering.