Thorsten Luettel

Autonomous Ground Vehicles—Concepts and a Path to the Future

Autonomous vehicles promise numerous improvements to vehicular traffic: an increase in both highway capacity and traffic flow because of faster response times, less fuel consumption and pollution thanks to more foresighted driving, and hopefully fewer accidents thanks to collision avoidance systems. In addition, drivers can save time for more useful activities. In order for these vehicles to safely operate in everyday traffic or in harsh off-road environments, a multitude of problems in perception, navigation, and control have to be solved. This paper gives an overview of the most current trends in autonomous vehicles, highlighting the concepts common to most successful systems as well as their differences. It concludes with an outlook into the promising future of autonomous vehicles.

Monocular model-based 3D vehicle tracking for autonomous vehicles in unstructured environment

In this paper we describe a novel approach to model-based monocular vehicle tracking out of a moving vehicle using active vision. The designed algorithm can cope with cluttered color images, complex lighting conditions as well as partial occlusion of the leading vehicle and is able to detect and track a vehicle even within unstructured offroad environments. Thanks to the used 3D model which describes the characteristic vehicle geometry and appearance in terms of vertexes, edges and colored surfaces, no special visual markers are required. The knowledge of vehicles geometry and appearance gained from the model are used within a particle filter to estimate the 6DoF position relative to the ego vehicle, thereby fusing edge as well as color information. We successfully use the proposed algorithm for pure vision based autonomous offroad convoy driving.

Autonomous Off-Road Navigation for MuCAR-3 Improving the Tentacles Approach: Integral Structures for Sensing and Motion

This report gives an overview of the autonomous navigation approach developed for the ground robot MuCAR-3, partly as a satellite project in the CoTeSys cluster of excellence. Safe robot navigation in general demands that the navigation approach can also cope with situations where GPS data is noisy or even absent and hence great care must be taken when using global map information. Choosing a safe action should be tightly coupled to the perception of the immediate surrounding in such situations. The tentacles approach developed earlier in the project efficiently deals with these issues by introducing integral structures for sensing and motion. This report presents the extensions and improvements made to the tentacles approach during the progress of the project and the results obtained at various challenging robot competitions.

Fusing vision and LIDAR - Synchronization, correction and occlusion reasoning

Autonomous navigation in unstructured environments like forest or country roads with dynamic objects remains a challenging task, particularly with respect to the perception of the environment using multiple different sensors.

Detection and tracking of road networks in rural terrain by fusing vision and LIDAR

The ability to perceive a robots local environment is one of the main challenges in the development of mobile ground robots. Here, we present a robust model-based approach for detection and tracking of road networks in rural terrain. To get a rich environment representation, we fuse the complementary data provided by a 3D LIDAR and an active camera platform into an accumulated, colored 3D elevation map of the terrain. Additionally, we use commercially available GIS data to get a rough idea about the geometry of the road network ahead of the robot. This way, the system is able to dynamically adjust the geometric model used within a particle filter framework for both detection and estimation of the road networks geometry. The estimation process makes use of edge- and region-based image features as well as obstacle information, all supplied by the dense terrain map. Instead of tuning the likelihood functions used within the particle filters cue fusion concept by hand, as commonly done, we apply supervised learning techniques to derive an appropriate weighting of all features. We finally show that the proposed approach enables our ground robot MuCAR-3 to autonomously navigate on rural- and dirt-road networks.

GIS-based topological robot localization through LIDAR crossroad detection

While navigating in areas with weak or erroneous GPS signals such as forests or urban canyons, correct map localization is impeded by means of contradicting position hypotheses. Thus, instead of just utilizing GPS positions improved by the robots ego-motion, this papers approach tries to incorporate crossroad measurements given by the robots perception system and topological informations associated to crossroads within a pre-defined road network into the localization process. We thus propose a new algorithm for crossroad detection in LIDAR data, that examines the free space between obstacles in an occupancy grid in combination with a Kalman filter for data association and tracking. Hence rather than correcting a robots position by just incorporating the robots ego-motion in the absence of GPS signals, our method aims at data association and correspondence finding by means of detected real world structures and their counterparts in predefined, maybe even handcrafted, digital maps.

Fusing LIDAR and Vision for Autonomous Dirt Road Following

In this paper we describe how visual features can be incorporated into the well known tentacles approach [1] which up to now has only used LIDAR and GPS data and was therefore limited to scenarios with significant obstacles or non-flat surfaces along roads. In addition we present a visual feature considering only color intensity which can be used to visually rate tentacles. The presented sensor fusion and color based feature were both applied with great success at the C-ELROB 2009 robotic competition.

Odometry-based online extrinsic sensor calibration

In recent years vehicles have been equipped with more and more sensors for environment perception. Among these sensors are cameras, RADAR, single-layer and multi-layer LiDAR. One key challenge for the fusion of these sensors is sensor calibration. In this paper we present a novel extrinsic calibration algorithm based on sensor odometry. Given the time-synchronized delta poses of two sensors our technique recursively estimates the relative pose between these sensors. The method is generic in that it can be used to estimate complete 6DOF poses, given the sensors provide a 6DOF odometry, as well as 3DOF poses (planar offset and yaw angle) for sensors providing a 3DOF odometry, like a single-beam LiDAR. We show that the proposed method is robust against motion degeneracy and present results on both simulated and real world data using an inertial navigation system (INS) and a stereo camera system.

Combining model- and template-based vehicle tracking for autonomous convoy driving

This paper presents a robust method for vehicle tracking with a monocular camera. A previously published model-based tracking method uses a particle filter which needs an initial vehicle hypothesis both at system start and in case of a tracking loss. We present a template-based solution using different features to estimate a 3D vehicle pose roughly but fast. Combining model- and template-based object tracking keeps the advantages of each algorithm: Precise estimation of the 3D vehicle pose and velocity combined with a fast (re-) initialization approach. The improved tracking system was evaluated while driving autonomously in urban and unstructured environments. The results show that poorly visible vehicles can be tracked during different weather conditions in real-time.

Combining multiple robot behaviors for complex off-road missions

This paper gives an overview of the autonomous off-road navigation approach developed for the ground robot vehicle MuCAR-3. It integrates our approaches to goal-directed and GIS-data supported autonomous navigation, autonomous person and vehicle following and shuttling on a taught track into a single system. The perceptual prerequisites necessary to realize the different robot behaviors are presented in detail, and we show how the individual behaviors available to our robot are coordinated to solve the complex off-road mission of the mule transport scenario at the European Land Robot Trials 2010. Finally, we give an impression of the systems performance by analyzing the results obtained at the trials.