For Lifelong SLAM, one has to deal with temporary localization failures, e.g., induced by kidnapping. We achieve this by starting a new map and merging it with the previous map as soon as relocalization succeeds. Since relocalization methods are fallible, it can happen that such a merge is invalid, e.g., due to perceptual aliasing. To address this issue, we propose methods to detect and undo invalid merges. These methods compare incoming scans with scans that were previously merged into the current map and consider how well they agree with each other. Evaluation of our methods takes place using a dataset that consists of multiple flat and office environments, as well as the public MIT Stata Center dataset. We show that methods based on a change detection algorithm and on comparison of gridmaps perform well in both environments and can be run in real-time with a reasonable computational cost.

Lidar-based SLAM systems perform well in a wide range of circumstances by relying on the geometry of the environment. However, even mature and reliable approaches struggle when the environment contains structureless areas such as long hallways. To allow the use of lidar-based SLAM in such environments, we propose to add reflector markers in specific locations that would otherwise be difficult. We present an algorithm to reliably detect these markers and two approaches to fuse the detected markers with geometry-based scan matching. The performance of the proposed methods is demonstrated on real-world datasets from several industrial environments.

Lifelong SLAM considers long-term operation of a robot where already mapped locations are revisited many times in changing environments. As a result, traditional graph-based SLAM approaches eventually become extremely slow due to the continuous growth of the graph and the loss of sparsity. Both problems can be addressed by a graph pruning algorithm. It carefully removes vertices and edges to keep the graph size reasonable while preserving the information needed to provide good SLAM results. We propose a novel method that considers geometric criteria for choosing the vertices to be pruned. It is efficient, easy to implement, and leads to a graph with evenly spread vertices that remain part of the robot trajectory. Furthermore, we present a novel approach of marginalization that is more robust to wrong loop closures than existing methods. The proposed algorithm is evaluated on two publicly available real-world long-term datasets and compared to the unpruned case as well as ground truth. We show that even on a long dataset (25h), our approach manages to keep the graph sparse and the speed high while still providing good accuracy (40 times speed up, 6cm map error compared to unpruned case).

In this paper, we present a novel nonlinear progressive filtering approach for estimatingSE(2) states represented by unit dual quaternions. Unlike previously published approaches, the measurement model no longer needs to be assumed as identity. Our solution utilizes deterministic sampling on a Bingham-like probability distribution, which has been adapted to simultaneously model orientation and translation. During the measurement update step, the estimate gets progressively updated. Our approach inherently incorporates the nonlinear structure ofSE(2) and enables a flexible measurement update step. We also give an evaluation for planar rigid body motion estimation with a case study that is close to real-world scenarios.

In this paper, we present a novel approach to perform simultaneous localization and mapping (SLAM) for planar motions based on stochastic filtering with dual quaternion particles using low-cost range and gyro sensor data. Here, SE(2) states are represented by unit dual quaternions and further get stochastically modeled by a distribution from directional statistics such that particles can be generated by random sampling. To build the full SLAM system, a novel dual quaternion particle filter based on Rao-Blackwellization is proposed for the tracking block, which is further integrated with an occupancy grid mapping block. Unlike previously proposed filtering approaches, our method can perform tracking in the presence of multi-modal noise in unknown environments while giving reasonable mapping results. The approach is further evaluated using a walking robot with on-board ultrasonic sensors and an IMU sensor navigating in an unknown environment in both simulated and real-world scenarios.

Many applications require state estimation where possible values of the state are constrained to an interval (say, the valve position in percent) or the unit circle (say, the direction a robot is facing). We present two approaches that rely on a discretization of the state space, which differ in their interpretation of the discretized density. The first option is a piecewise constant density and the second option is a Dirac-mixture density. We show how circular filters can be derived and discuss the advantages and disadvantages of both approaches. In addition, we show how to extend the Dirac-based approach to estimation on the special Euclidean group in 2D, the group of rigid body motions in the plane, using Rao-Blackwellization. All presented the methods are thoroughly evaluated in simulations.

Estimation of periodic quantities such as angles or phase values is a common problem. However, standard approaches, for example the Kalman filter and extensions thereof, have difficulties when estimating periodic quantities. To address this problem, circular filtering algorithms have been proposed but they are limited to just a single angle. In order to deal with multiple, possibly correlated angles, toroidal filtering algorithms are necessary. We have previously proposed a bivariate filtering algorithm on the torus [1] that is limited to identity system and measurement models. In this paper, we show how the algorithm can be extended to handle nonlinear system and measurement models. The novel approach relies on the bivariate wrapped normal distribution for representing the uncertainty and it makes use of a deterministic sampling scheme for the torus. We provide a thorough evaluation of the proposed method using simulations.

We introduce the unscented von Mises-Fisher filter (UvMFF), a nonlinear filtering algorithm for dynamic state estimation on the $n$-dimensional unit hypersphere. Estimation problems on the unit hypersphere occur in computer vision, e.g., when using omnidirectional cameras, as well as in signal processing. As approaches in literature are limited to very simple system and measurement models, we propose a deterministic sampling scheme on the unit hypersphere, which allows us to handle nonlinear system and measurement models. The proposed approach can be seen as a hyperspherical variant of the unscented Kalman filter (UKF). The advantages of the novel method are shown by means of simulations.

In this paper, we present a novel approach for tracking objects whose movement is constrained to a compact one-dimensional manifold, for example a conveyer belt or a mobile robot whose movement is restricted to tracks. Standard approaches either ignore the constraint at first and retroactively move the estimate to lie on the manifold, or consider the tracking problem on a manifold but falsely assume a Gaussian distribution. Our method explicitly takes the actual topology into account from the beginning and relies on special types of probability distributions defined on the proper manifold. In particular, we consider objects moving along a closed one-dimensional track, for example an ellipse, a polygon, or similar closed shapes. This shape is transformed to a circle with a homeomorphism. Thus, we can apply a recursive circular filtering algorithm to the constrained tracking problem. Finally, the estimate is transformed back to the original manifold. We evaluate the proposed method in an experiment by tracking a toy train moving along a track and comparing the results to those of traditional approaches for this problem.