There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

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

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Visual understanding of complex urban street scenes is an enabling factor for a wide range of applications. Object detection has benefited enormously from large-scale datasets, especially in the context of deep learning. For semantic urban scene understanding, however, no current dataset adequately captures the complexity of real-world urban scenes. To address this, we introduce Cityscapes, a benchmark suite and large-scale dataset to train and test approaches for pixel-level and instance-level semantic labeling. Cityscapes is comprised of a large, diverse set of stereo video sequences recorded in streets from 50 different cities. 5000 of these images have high quality pixel-level annotations, 20 000 additional images have coarse annotations to enable methods that leverage large volumes of weakly-labeled data. Crucially, our effort exceeds previous attempts in terms of dataset size, annotation richness, scene variability, and complexity. Our accompanying empirical study provides an in-depth analysis of the dataset characteristics, as well as a performance evaluation of several state-of-the-art approaches based on our benchmark.

https://www.cityscapes-dataset.com/wordpress/wp-content/papercite-data/pdf/cordts2016cityscapes.pdf

The Cityscapes Dataset for Semantic Urban Scene Understanding

We present a novel dataset captured from a VW station wagon for use in mobile robotics and autonomous driving research. In total, we recorded 6 hours of traffic scenarios at 10-100 Hz using a variety of sensor modalities such as high-resolution color and grayscale stereo cameras, a Velodyne 3D laser scanner and a high-precision GPS/IMU inertial navigation system. The scenarios are diverse, capturing real-world traffic situations, and range from freeways over rural areas to inner-city scenes with many static and dynamic objects. Our data is calibrated, synchronized and timestamped, and we provide the rectified and raw image sequences. Our dataset also contains object labels in the form of 3D tracklets, and we provide online benchmarks for stereo, optical flow, object detection and other tasks. This paper describes our recording platform, the data format and the utilities that we provide.

/pdf/vision-meets-robotics-the-kitti-dataset-2h3kd0lpgn.pdf

Vision meets robotics: The KITTI dataset

This paper is about robot ego-motion estimation relying solely on acoustic sensing. By equipping a robot with microphones, we investigate the possibility of employing the noise generated by the motors and actuators of the vehicle to estimate its motion. Audio-based odometry is not affected by the scene’s appearance, lighting conditions, and structure. This makes sound a compelling auxiliary source of information for ego-motion modelling in environments where more traditional methods, such as those based on visual or laser odometry, are particularly challenged. By leveraging multi-task learning and deep architectures, we provide a regression framework able to estimate the linear and the angular velocity at which the robot has been travelling. Our experimental evaluation conducted on approximately two hours of data collected with an unmanned outdoor field robot demonstrated an absolute error lower than 0.07 m/s and 0.02 rad/s for the linear and angular velocity, respectively. When compared to a baseline approach, making use of single-task learning scheme, our system shows an improvement of up to 26% in the ego-motion estimation.

/pdf/learning-to-listen-to-your-ego-motion-metric-motion-3p4gla5efh.pdf

Learning to Listen to Your Ego-(motion): Metric Motion Estimation from Auditory Signals

Traditional approaches to outdoor vehicle localization assume a reliable, prior map is available, typically built using the same sensor suite as the on-board sensors used during localization. This ...

https://journals.sagepub.com/doi/pdf/10.1177/02783649211045736

Self-supervised learning for using overhead imagery as maps in outdoor range sensor localization:

The availability of a large quantity of labelled training data is crucial for the training of modern object detectors. Hand labelling training data is time consuming and expensive while automatic labelling methods inevitably add unwanted noise to the labels. We examine the effect of different types of label noise on the performance of an object detector. We then show how co-teaching, a method developed for handling noisy labels and previously demonstrated on a classification problem, can be improved to mitigate the effects of label noise in an object detection setting. We illustrate our results using simulated noise on the KITTI dataset and on a vehicle detection task using automatically labelled data.

Training Object Detectors With Noisy Data.

The design of mobile autonomous robots is challenging due to the limited on-board resources such as processing power and energy. A promising approach is to generate intelligent schedules that reduce the resource consumption while maintaining best performance, or more interestingly, to tradeoff reduced resource consumption for a slightly lower but still acceptable level of performance. In this letter, we provide a framework that is automatic and quantitative to aid designers in exploring such resource-performance tradeoffs and finding schedules for mobile robots, guided by questions such as “what is the minimum resource budget required to achieve a given level of performance?” The framework is based on a quantitative multiobjective verification technique, which, for a collection of possibly conflicting objectives, produces the Pareto front that contains all the achievable optimal tradeoffs. The designer then selects a specific Pareto point based on the resource constraints and desired performance level, and a correct-by-construction schedule that meets those constraints is automatically generated. We demonstrate the efficacy of this framework on several robotic scenarios in both simulations and experiments.

/pdf/resource-performance-tradeoff-analysis-for-mobile-robots-1x8hr1pzvz.pdf

Resource-Performance Tradeoff Analysis for Mobile Robots

Road boundaries, or curbs, provide autonomous vehicles with essential information when interpreting road scenes and generating behaviour plans. Although curbs convey important information, they are difficult to detect in complex urban environments (in particular in comparison to other elements of the road such as traffic signs and road markings). These difficulties arise from occlusions by other traffic participants as well as changing lighting and/or weather conditions. Moreover, road boundaries have various shapes, colours and structures while motion planning algorithms require accurate and precise metric information in real-time to generate their plans.In this paper, we present a real-time LIDAR-based approach for accurate curb detection around the vehicle (360 degree). Our approach deals with both occlusions from traffic and changing environmental conditions. To this end, we project 3D LIDAR pointcloud data into 2D bird’s-eye view images (akin to Inverse Perspective Mapping). These images are then processed by trained deep networks to infer both visible and occluded road boundaries. Finally, a post-processing step filters detected curb segments and tracks them over time. Experimental results demonstrate the effectiveness of the proposed approach on real-world driving data. Hence, we believe that our LIDAR-based approach provides an efficient and effective way to detect visible and occluded curbs around the vehicles in challenging driving scenarios.

Paul Newman

Papers

Learning to Listen to Your Ego-(motion): Metric Motion Estimation from Auditory Signals

Self-supervised learning for using overhead imagery as maps in outdoor range sensor localization:

Training Object Detectors With Noisy Data.

Resource-Performance Tradeoff Analysis for Mobile Robots

Online Inference and Detection of Curbs in Partially Occluded Scenes with Sparse LIDAR