This paper reports on a data-driven, interaction-aware motion prediction approach for pedestrians in environments cluttered with static obstacles. When navigating in such workspaces shared with humans, robots need accurate motion predictions of the surrounding pedestrians. Human navigation behavior is mostly influenced by their surrounding pedestrians and by the static obstacles in their vicinity. In this paper we introduce a new model based on Long-Short Term Memory (LSTM) neural networks, which is able to learn human motion behavior from demonstrated data. To the best of our knowledge, this is the first approach using LSTMs, that incorporates both static obstacles and surrounding pedestrians for trajectory forecasting. As part of the model, we introduce a new way of encoding surrounding pedestrians based on a 1d-grid in polar angle space. We evaluate the benefit of interaction-aware motion prediction and the added value of incorporating static obstacles on both simulation and real-world datasets by comparing with state-of-the-art approaches. The results show, that our new approach outperforms the other approaches while being very computationally efficient and that taking into account static obstacles for motion predictions significantly improves the prediction accuracy, especially in cluttered environments.

Using multiple cooperative robots is advantageous for time critical Search and Rescue (SaR) missions as they permit rapid exploration of the environment and provide higher redundancy than using a single robot. A considerable number of applications such as autonomous driving and disaster response could benefit from merging mapping data from several agents. Online multi-robot localization and mapping has mainly been addressed for robots equipped with cameras or 2D LiDARs. However, in unstructured and ill-lighted real-life scenarios, a mapping system can potentially benefit from a rich 3D geometric solution. In this work, we present an online localization and mapping system for multiple robots equipped with 3D LiDARs. This system is based on incremental sparse pose-graph optimization using sequential and place recognition constraints, the latter being identified using a 3D segment matching approach. The result is a unified representation of the world and relative robot trajectories. The complete system runs in real-time and is evaluated with two experiments in different environments: one urban and one disaster scenario. The system is available open source and easy-to-run demonstrations are publicly available.

Deployment of camera and laser based motion estimation systems for controlling platforms operating at high speeds, such as cars or trains, is posing increasingly challenging precision requirements on the temporal calibration of these sensors. In this work, we demonstrate a simple, low-cost system for calibrating any combination of cameras and time of flight LIDARs with respect to the CPU clock (and therefore, also to each other). The newly proposed device is based on widely available off-the-shelf components, such as the Raspberry Pi 3, which is synchronized using the Precision Time Protocol (PTP) with respect to the CPU of the sensor carrying system. The obtained accuracy can be shown to be below 0.1 ms per measurement for LIDARs and below minimal exposure time per image for cameras. It outperforms state-of-the-art approaches also not relying on hardware synchronization by more than a factor of 10 in precision. Moreover, the entire process can be carried out at a high rate allowing the study of how offsets evolve over time. In our analysis, we demonstrate how each building block of the system contributes to this accuracy and validate the obtained results using real-world data.

Sliding window estimation is widely used for online simultaneous localization and mapping. While increasing the sliding window size generally yields improved accuracy, it also comes at an increase in computational cost. In order to reduce this cost, we propose smarter non-uniform sampling of the trajectory representation over the sliding window. This non-uniform temporal resolution is possible with continuous-time representations that allow freely adjustable knots location. Four strategies for selecting the knots location are presented and evaluated based on a real data laser-odometry SLAM problem. The results clearly show that non-uniform distributions of knots can be superior to uniform distribution in terms of accuracy per computation time.

This paper reports on a data-driven motion planning approach for interaction-aware, socially-compliant robot navigation among human agents. Autonomous mobile robots navigating in workspaces shared with human agents require motion planning techniques providing seamless integration and smooth navigation in such. Smooth integration in mixed scenarios calls for two abilities of the robot: predicting actions of others and acting predictably for them. The former requirement requests trainable models of agent behaviors in order to accurately forecast their actions in the future, taking into account their reaction on the robot's decisions. A human-like navigation style of the robot facilitates other agents-most likely not aware of the underlying planning technique applied-to predict the robot motion vice versa, resulting in smoother joint navigation. The approach presented in this paper is based on a feature-based maximum entropy model and is able to guide a robot in an unstructured, real-world environment. The model is trained to predict joint behavior of heterogeneous groups of agents from onboard data of a mobile platform. We evaluate the benefit of interaction-aware motion planning in a realistic public setting with a total distance traveled of over 4 km. Interestingly the motion models learned from human-human interaction did not hold for robot-human interaction, due to the high attention and interest of pedestrians in testing basic braking functionality of the robot.

In this paper we present a method for fusing optical flow and inertial measurements. To this end, we derive a novel visual error term which is better suited than the standard continuous epipolar constraint for extracting the information contained in the optical flow measurements. By means of an unscented Kalman filter (UKF), this information is then tightly coupled with inertial measurements in order to estimate the egomotion of the sensor setup. The individual visual landmark positions are not part of the filter state anymore. Thus, the dimensionality of the state space is significantly reduced, allowing for a fast online implementation. A nonlinear observability analysis is provided and supports the proposed method from a theoretical side. The filter is evaluated on real data together with ground truth from a motion capture system.