Daniel Althoff

Comparison of surface normal estimation methods for range sensing applications

As mobile robotics is gradually moving towards a level of semantic environment understanding, robust 3D object recognition plays an increasingly important role. One of the most crucial prerequisites for object recognition is a set of fast algorithms for geometry segmentation and extraction, which in turn rely on surface normal vectors as a fundamental feature. Although there exists a plethora of different approaches for estimating normal vectors from 3D point clouds, it is largely unclear which methods are preferable for online processing on a mobile robot. This paper presents a detailed analysis and comparison of existing methods for surface normal estimation with a special emphasis on the trade-off between quality and speed. The study sheds light on the computational complexity as well as the qualitative differences between methods and provides guidelines on choosing the ‘right’ algorithm for the robotics practitioner. The robustness of the methods with respect to noise and neighborhood size is analyzed. All algorithms are benchmarked with simulated as well as real 3D laser data obtained from a mobile robot.

Safety assessment of robot trajectories for navigation in uncertain and dynamic environments

This paper presents a probabilistic framework for reasoning about the safety of robot trajectories in dynamic and uncertain environments with imperfect information about the future motion of surrounding objects. For safety assessment, the overall collision probability is used to rank candidate trajectories by considering the probability of colliding with known objects as well as the estimated collision probability beyond the planning horizon. In addition, we introduce a safety assessment cost metric, the probabilistic collision cost, which considers the relative speeds and masses of multiple moving objects in which the robot may possibly collide with. The collision probabilities with other objects are estimated by probabilistic reasoning about their future motion trajectories as well as the ability of the robot to avoid them. The results are integrated into a navigation framework that generates and selects trajectories that strive to maximize safety while minimizing the time to reach a goal location. An example implementation of the proposed framework is applied to simulation scenarios, that explores some of the inherent computational trade-offs.

Probabilistic collision state checker for crowded environments

For path planning algorithms of robots it is important that the robot does not reach a state of inevitable collision. In crowded environments with many humans or robots, the set of possible inevitable collision states (ICS) is often unacceptably high, such that the robot has to stop and wait in too many situations. For this reason, the concept of ICS is extended to probabilistic collision states (PCS), which estimates the collision probability for a given state. This allows to efficiently run planning algorithms through crowded environments when accepting a certain collision probability. A further novelty is that the obstacles possibly react to the robot in order to mitigate the risk of a collision. The results show a significant difference in interaction behavior. Thus, this approach is especially suited for active and non-deterministic moving obstacles in the robot workspace.

Safety verification of autonomous vehicles for coordinated evasive maneuvers

The verification of evasive maneuvers for autonomous vehicles driving with constant velocity is considered. Modeling uncertainties, uncertain measurements, and disturbances can cause substantial deviations from an initially planned evasive maneuver. From this follows that the maneuver, which is safe under perfect conditions, might become unsafe. In this work, the possible set of deviations is computed with methods from reachability analysis, which allows to verify evasive maneuvers under consideration of the mentioned uncertainties. Since the presented approach has a short response time, it can be applied for real time safety decisions. The methods are presented for a numerical example where two autonomous cars plan a coordinated evasive maneuver in order to prevent a collision with a wrong-way driver.

Interactive scene prediction for automotive applications

In this work, a framework for motion prediction of vehicles and safety assessment of traffic scenes is presented. The developed framework can be used for driver assistant systems as well as for autonomous driving applications. In order to assess the safety of the future trajectories of the vehicle, these systems require a prediction of the future motion of all traffic participants. As the traffic participants have a mutual influence on each other, the interaction of them is explicitly considered in this framework, which is inspired by an optimization problem. Taking the mutual influence of traffic participants into account, this framework differs from the existing approaches which consider the interaction only insufficiently, suffering reliability in real traffic scenes. For motion prediction, the collision probability of a vehicle performing a certain maneuver, is computed. Based on the safety evaluation and the assumption that drivers avoid collisions, the prediction is realized. Simulation scenarios and real-world results show the functionality.

Safety assessment of trajectories for navigation in uncertain and dynamic environments

This paper presents a probabilistic threat assessment method for reasoning about the safety of robot trajectories in uncertain and dynamic environments. For safety evaluation, the overall collision probability is used to rank candidate trajectories by considering the collision probability of known objects as well as the collision probability beyond the planning horizon. Monte Carlo sampling is used to estimate the collision probabilities. This concept is applied to a navigation framework that generates and selects trajectories in order to reach the goal location while minimizing the collision probability. Simulation scenarios are used to validate the overall crash probability and show its necessity in the proposed navigation approach.

An Architecture for Real-Time Control in Multi-robot Systems

This paper presents a novel robotic architecture that is suitable for modular distributed multi-robot systems. The architecture is based on an interface supporting real-time inter-process communication, which allows simple and highly efficient data exchange between the modules. It allows monitoring of the internal system state and easy logging, thus facilitating the module development. The extension to distributed systems is provided through a communication middleware, which enables fast and transparent exchange of data through the network, although without real-time guarantees. The advantages and disadvantages of the architecture are rated using an existing framework for evaluation of robot architectures.

Lane-based safety assessment of road scenes using Inevitable Collision States

This paper presents a method for reasoning about the safety of traffic situations. More precisely, the problem of safety assessment for partial trajectories for vehicles is addressed. Therefore, the Inevitable Collision States (ICS) as well as its probabilistic generalization the Probabilistic Collision States (PCS) are used. Thereby, the assessment is performed for an infinite time horizon. For solving the ICS computation nonlinear programming is applied. In addition to the safety assessment an evaluation of the disturbance of the other traffic participants by the ego vehicle is presented. The results are integrated into an optimal control based planning approach that generates minimum jerk trajectories. An example implementation of the proposed framework is applied to simulation scenarios that demonstrates the necessity of the presented method for guaranteeing motion safety.

On-line Trajectory Generation for Safe and Optimal Vehicle Motion Planning

This paper presents a framework for motion planning of autonomous vehicles, it is characterized by its efficient computation and its safety guarantees. An optimal control based approach generates comfortable and physically feasible maneuvers of the vehicle. Therefore, a combined optimization of the lateral and longitudinal movements in street-relative coordinates with carefully chosen cost functionals and terminal state sets is performed. The collision checking of the trajectories during the planning horizon is also performed in street-relative coordinates. It provides continuous collision checking, which covers nearly all situations based on an algebraic solution and has a constant response time. Finally, the problem of safety assessment for partial trajectories beyond the planning horizon is addressed. Therefore, the Inevitable Collision States (ICS) are used, extending the safety assessment to an infinite time horizon. To solve the ICS computation nonlinear programming is applied. An example implementation of the proposed framework is applied to simulation scenarios that demonstrates its efficiency and safety capabilities.

Computing unions of Inevitable Collision States and increasing safety to unexpected obstacles

For reasoning about the safety of a robot system, it is sufficient to pretend the robot to reach an Inevitable Collision Sate (ICS). Otherwise, there exists no future trajectory which can avoid a collision. The usage of ICS is limited due to its computational complexity. One reason for this is, that the ICS computation cannot be done separately for each obstacle. Hence, ICS needs to be recomputed from scratch if another object appears in the scene. The main contribution of this paper is a modified ICS calculation which allows to compute the union of ICS sets in a sequential manner, thus reducing the computational requirements in case of new obstacles. Therefore, two novel ICS-Checker algorithms are presented reducing the computational effort. Furthermore, this novel calculation is used to reduce the probability of being in an ICS regarding an unforeseen obstacle.