Long-term autonomous mobile robot operation requires considering place recognition decisions with great caution. A single incorrect decision that is not detected and reconsidered can corrupt the environment model that the robot is trying to build and maintain. This work describes a consensus-based approach to robust place recognition over time, that takes into account all the available information to detect and remove past incorrect loop closures. The main novelties of our work are: (1) the ability of realizing that, in light of new evidence, an incorrect past loop closing decision has been made; the incorrect information can be removed thus recovering the correct estimation with a novel algorithm; (2) extending our proposal to incremental operation; and (3) handling multi-session, spatially related or unrelated scenarios in a unified manner. We demonstrate our proposal, the RRR algorithm, on different odometry systems, e.g. visual or laser, using different front-end loop-closing techniques. For our experiments we use the efficient graph optimization framework g2o as back-end. We back our claims up with several experiments carried out on real data, in single and multi-session experiments showing better results than those obtained by state-of-the-art methods, comparisons against whom are also presented.

The ability to reconsider information over time allows to detect failures and is crucial for long term robust autonomous robot applications. This applies to loop closure decisions in localization and mapping systems. This paper describes a method to analyze all available information up to date in order to robustly remove past incorrect loop closures from the optimization process. The main novelties of our algorithm are: 1. incrementally reconsidering loop closures and 2. handling multi-session, spatially related or unrelated experiments. We validate our proposal in real multi-session experiments showing better results than those obtained by state of the art methods.

In this paper we investigate the monotonicity of various optimality criteria during the exploration phase of an active SLAM algorithm. Optimality criteria such as A-opt, D-opt or E-opt are used in active SLAM to account for uncertainty in the map or the robot's pose, and these criteria are usually part of utility functions which help active SLAM algorithms decide where the robot should move next. The monotonicity of the optimality criteria is of utmost importance. During the exploration phase, i.e. when the robot is traversing new territory or cannot perform a loop closure, the most common way of estimating the pose of the robot is through dead-reckoning. Correctly accounting for the uncertainty is important for an active SLAM algorithm and in particular for a dead-reckoning scenario, where by definition the uncertainty in the robot's pose grows. If monotonicity does not hold in this scenario, active SLAM algorithms can execute actions under the false belief that the uncertainty has reduced. We show analytically and experimentally some conditions in which the A-opt and E-opt criteria lose monotonicity in a dead-reckoning scenario, where the propagation of the robot's pose is done using a linearized framework. We also show analytically and experimentally that under the same conditions the D-opt does not lose monotonicity and, in general for the linearized framework under consideration, D-opt does not break monotonicity.

In this work, we provide an in-depth analysis of several recent robust Simultaneous Localization And Mapping (SLAM) back-end techniques that aim to recover the correct graph estimate in the presence of outliers in loop closure constraints. We present a benchmark dataset for evaluation of such methods by augmenting the KITTI Vision Benchmark with ground truth as well as generated loop closure hypotheses and present a detailed analysis of recently proposed robust SLAM methods using this benchmark. We also look into how these methods achieve the desired robustness and what are the implications for the SLAM problem. We discuss the issues involved in using the output of these robust back-ends for tasks such as path planning and how they can be addressed. The problem of robustness needs to be addressed adequately in order to have a complete and reliable solution to the SLAM problem.