Simultaneous Localization and Mapping (SLAM) is one of the basic problems in mobile robotics. While most approaches are based on occupancy grid maps, Normal Distributions Transforms (NDT) and mixtures like Occupancy Normal Distribution Transforms (ONDT) have been shown to represent sensor measurements more accurately. In this work, we slightly re-formulate the (O)NDT matching function such that it becomes a least squares problem that can be solved with various robust numerical and analytical non-linear optimizers. Further, we propose a novel global (O)NDT scan matcher for loop closure. In our evaluation, our NDT and ONDT methods are able to outperform the occupancy grid map based ones we adopted from Google's Cartographer implementation.

Recursive state estimation is often used to estimate a probability density function of a specific state, e.g. a robot's pose, over time. Compared to Kalman filters, Sequential Monte Carlo (SMC) methods are less constrained in regard to state propagation and update model definition, which makes it easier to implement any suitable problem. In this work, we present a generic Sequential Monte Carlo framework, which uses abstract formulations for importance weighting, propagation and resampling and provides an independent core algorithm that is usable for any problem instantiation, such that diverse SMC problems can be implemented easily and quickly, since the basic algorithms are already provided. Current applications include 2D localization, 2D tracking in a SLAM system and contact point localization on a manipulator surface. Further, we introduce concepts to deal with data input synchronization and fair execution of different weighting models, which makes it possible to incorporate data from as many update sources, e.g. sensors, as desired. As a typical application scenario, we provide a plugin-based and hence easily extensible instantiation for 2D localization and demonstrate the capabilities of our framework and methods based on a well-known dataset.

Many robotic applications, especially exploration scenarios, benefit from deploying multiple collaborating robots with the aim of parallelizing and therefore accelerating the involved task. One critical part of a multi-robot system is its communication system. Depending on the application scenario, high-bandwidth wireless connections, such as WiFi, may not always be available and suffer from a limited communication range. On the other hand, low-bandwith systems require the application itself to deal with limited information exchange. In this work, we present a novel approach for collaborative mapping using mixtures of occupancy and NDT maps (called ONDT), which provide detailed information at low resolutions and in which the pose uncertainty can be encoded efficiently. Further, we compare the applicability of three different radio frequency modules operating at different frequencies in real world experiments with five robots at large distances.

Detecting and localizing contacts acting on a manipulator is a relevant problem for manipulation tasks like grasping, since contact information can be helpful for recovering from collisions or for improving the grasping performance itself. In this work, we present a solution for contact point localization, which is based on Monte Carlo Localization. Usually, an Articulated Robotic Manipulator (ARM) is not equipped with tactile skin, but with proprioceptive sensors, which we assume as an input for our method. In our experiments, we compare our method with a direct optimization method, machine learning approaches and another particle filter method, both on simulated and real world data from a Kinova Jaco2. While our proposed method clearly outperforms the other optimization approaches, it performs about equally well as Random Forest (RF) classifiers, although both methods have their strengths on different parts of the manipulator, and even achieves better results than multi-layer perceptions (MLPs) on the links farthest from the manipulator base.

Efficient 2D and 3D map representations of both static and dynamic, indoor and outdoor environments are crucial for navigation of driving and flying robots. In this paper, we propose a fast and accurate approach for 2D and 3D Normal Distributions Transform (NDT) mapping based on indexed kd-trees. Similar to other approaches, we also model free space, which allows us to obtain occupancy probabilities. Additionally, we provide optional visibility based updates to enhance map consistency in case of noisy data, e.g. from stereo cameras. Unlike other available implementations, our approach is natively applicable to large-scale environments and in real-time, because our maps are able to grow dynamically. This also offers applicability to exploration tasks. To evaluate our approach, we present experimental results on publicly available datasets and discuss the mapping efficiency in terms of accuracy, runtime and memory management. As an exemplary use case, we apply our maps to Monte Carlo Localization on a well-known large-scale dataset.