Today, visual recognition systems are still rarely employed in robotics applications. Perhaps one of the main reasons for this is the lack of demanding benchmarks that mimic such scenarios. In this paper, we take advantage of our autonomous driving platform to develop novel challenging benchmarks for the tasks of stereo, optical flow, visual odometry/SLAM and 3D object detection. Our recording platform is equipped with four high resolution video cameras, a Velodyne laser scanner and a state-of-the-art localization system. Our benchmarks comprise 389 stereo and optical flow image pairs, stereo visual odometry sequences of 39.2 km length, and more than 200k 3D object annotations captured in cluttered scenarios (up to 15 cars and 30 pedestrians are visible per image). Results from state-of-the-art algorithms reveal that methods ranking high on established datasets such as Middlebury perform below average when being moved outside the laboratory to the real world. Our goal is to reduce this bias by providing challenging benchmarks with novel difficulties to the computer vision community. Our benchmarks are available online at: www.cvlibs.net/datasets/kitti

/pdf/are-we-ready-for-autonomous-driving-the-kitti-vision-zygefho352.pdf

Are we ready for autonomous driving? The KITTI vision benchmark suite

Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

In this paper, we present a novel benchmark for the evaluation of RGB-D SLAM systems. We recorded a large set of image sequences from a Microsoft Kinect with highly accurate and time-synchronized ground truth camera poses from a motion capture system. The sequences contain both the color and depth images in full sensor resolution (640 × 480) at video frame rate (30 Hz). The ground-truth trajectory was obtained from a motion-capture system with eight high-speed tracking cameras (100 Hz). The dataset consists of 39 sequences that were recorded in an office environment and an industrial hall. The dataset covers a large variety of scenes and camera motions. We provide sequences for debugging with slow motions as well as longer trajectories with and without loop closures. Most sequences were recorded from a handheld Kinect with unconstrained 6-DOF motions but we also provide sequences from a Kinect mounted on a Pioneer 3 robot that was manually navigated through a cluttered indoor environment. To stimulate the comparison of different approaches, we provide automatic evaluation tools both for the evaluation of drift of visual odometry systems and the global pose error of SLAM systems. The benchmark website [1] contains all data, detailed descriptions of the scenes, specifications of the data formats, sample code, and evaluation tools.

/pdf/a-benchmark-for-the-evaluation-of-rgb-d-slam-systems-4sau0k4128.pdf

A benchmark for the evaluation of RGB-D SLAM systems

This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Coverage control for mobile sensing networks

Three-dimensional models provide a volumetric representation of space which is important for a variety of robotic applications including flying robots and robots that are equipped with manipulators. In this paper, we present an open-source framework to generate volumetric 3D environment models. Our mapping approach is based on octrees and uses probabilistic occupancy estimation. It explicitly represents not only occupied space, but also free and unknown areas. Furthermore, we propose an octree map compression method that keeps the 3D models compact. Our framework is available as an open-source C++ library and has already been successfully applied in several robotics projects. We present a series of experimental results carried out with real robots and on publicly available real-world datasets. The results demonstrate that our approach is able to update the representation efficiently and models the data consistently while keeping the memory requirement at a minimum.

/pdf/octomap-an-efficient-probabilistic-3d-mapping-framework-3dp42nnofi.pdf

OctoMap: an efficient probabilistic 3D mapping framework based on octrees

In this paper, we address the problem of creating an objective benchmark for evaluating SLAM approaches. We propose a framework for analyzing the results of a SLAM approach based on a metric for measuring the error of the corrected trajectory. This metric uses only relative relations between poses and does not rely on a global reference frame. This overcomes serious shortcomings of approaches using a global reference frame to compute the error. Our method furthermore allows us to compare SLAM approaches that use different estimation techniques or different sensor modalities since all computations are made based on the corrected trajectory of the robot.

We provide sets of relative relations needed to compute our metric for an extensive set of datasets frequently used in the robotics community. The relations have been obtained by manually matching laser-range observations to avoid the errors caused by matching algorithms. Our benchmark framework allows the user to easily analyze and objectively compare different SLAM approaches.

/pdf/on-measuring-the-accuracy-of-slam-algorithms-4nkbhz341e.pdf

On measuring the accuracy of SLAM algorithms

Car pollution is one of the major causes of greenhouse emissions, and traffic congestion is rapidly becoming a social plague. Dynamic Ride Sharing (DRS) systems have the potential to mitigate this problem by computing plans for car drivers, e.g. commuters, allowing them to share their rides. Existing efforts in DRS are suffering from the problem that participants are abandoning the system after repeatedly failing to get a shared ride. In this paper we present an incentive compatible DRS solution based on auctions. While existing DRS systems are mainly focusing on fixed assignments that minimize the totally travelled distance, the presented approach is adaptive to individual preferences of the participants. Furthermore, our system allows to tradeoff the minimization of Vehicle Kilometers Travelled (VKT) with the overall probability of successful ride-shares, which is an important feature when bootstrapping the system. To the best of our knowledge, we are the first to present a DRS solution based on auctions using a sealed-bid second price scheme.

/pdf/a-mechanism-for-dynamic-ride-sharing-based-on-parallel-4he1ogrags.pdf

A mechanism for dynamic ride sharing based on parallel auctions

Robotic soccer is a challenging research domain because many different research areas have to be addressed in order to create a successful team of robot players. The paper presents the CS Freiburg team, the winner in the middle-size league at RoboCup 1998, 2000, and 2001. The paper focuses on multiagent coordination for both perception and action. The contributions of the paper are new methods for tracking ball and players observed by multiple robots, team coordination methods for strategic team formation and dynamic role assignment; a rich set of basic skills allowing robots to respond to a large range of situations in an appropriate way, and an action-selection method based on behavior networks, as well as a method to learn the skills and their selection. As demonstrated by evaluations of the different methods and by the success of the team, these methods permit the creation of a multirobot group which is able to play soccer successfully. In addition, the developed methods promise to advance the state of the art in the multirobot field.

/pdf/cs-freiburg-coordinating-robots-for-successful-soccer-2is4jb3z3z.pdf

CS Freiburg: coordinating robots for successful soccer playing

The problem of learning a map with a mobile robot has been intensively studied in the past and is usually referred to as the simultaneous localization and mapping (SLAM) problem. However, most existing solutions to the SLAM problem learn the maps from scratch and have no means for incorporating prior information. In this paper, we present a novel SLAM approach that achieves global consistency by utilizing publicly accessible aerial photographs as prior information. It inserts correspondences found between stereo and three-dimensional range data and the aerial images as constraints into a graph-based formulation of the SLAM problem. We evaluate our algorithm based on large real-world datasets acquired even in mixed in- and outdoor environments by comparing the global accuracy with state-of-the-art SLAM approaches and GPS. The experimental results demonstrate that the maps acquired with our method show increased global consistency.

/pdf/large-scale-graph-based-slam-using-aerial-images-as-prior-1did75y24m.pdf

Large scale graph-based SLAM using aerial images as prior information

In autonomous multirobot systems one of the concerns is how to prevent collisions between the individual robots. One approach to this problem involves finding coordinated trajectories from start to destination for all the robots and then letting the robots follow the preplanned coordinated trajectories. A widely used practical method for finding such coordinated trajectories is “classical” prioritized planning, where robots plan sequentially one after another. This method has been shown to be effective in practice, but it is incomplete (i.e., there are solvable problem instances that the algorithm fails to solve) and it has not yet been formally analyzed under what circumstances is the method guaranteed to succeed. Further, prioritized planning is a centralized algorithm, which makes the method unsuitable for decentralized multirobot systems.

/pdf/prioritized-planning-algorithms-for-trajectory-coordination-gkrxi6s6rh.pdf

Alexander Kleiner

Papers

On measuring the accuracy of SLAM algorithms

A mechanism for dynamic ride sharing based on parallel auctions

CS Freiburg: coordinating robots for successful soccer playing

Large scale graph-based SLAM using aerial images as prior information

Prioritized Planning Algorithms for Trajectory Coordination of Multiple Mobile Robots