Semantic 3D Occupancy Mapping through Efficient High Order CRFs

Semantic 3D mapping can be used for many applications such as robot navigation and virtual interaction. In recent years, there has been great progress in semantic segmentation and geometric 3D mapping. However, it is still challenging to combine these two tasks for accurate and large-scale semantic mapping from images. In the paper, we propose an incremental and (near) real-time semantic mapping system. A 3D scrolling occupancy grid map is built to represent the world, which is memory and computationally efficient and bounded for large scale environments. We utilize the CNN segmentation as prior prediction and further optimize 3D grid labels through a novel CRF model. Superpixels are utilized to enforce smoothness and form robust P N high order potential. An efficient mean field inference is developed for the graph optimization. We evaluate our system on the KITTI dataset and improve the segmentation accuracy by 10% over existing systems.Comment: IROS 201

Multi-Support Gaussian Processes for Continuous Occupancy Mapping

Robotic mapping enables an autonomous agent to build a representation of its environment based upon sensorial information. In particular, occupancy mapping aims at classifying regions of space according to whether or not they are occupied---and, therefore, inaccessible to the agent. Traditional techniques rely on discretisation to perform this task. The problems tackled by this thesis stem from the discretisation of continuous phenomena and from the inherently inaccurate and large datasets typically created by state-of-the-art robotic sensors. To approach this challenge, we make use of statistical modelling to handle the noise in the data and create continuous occupancy maps. The proposed approach makes use of Gaussian processes, a non-parametric Bayesian inference framework that uses kernels, to handle sensor noise and learn the dependencies among data points. The main drawback is the method's computational complexity, which grows cubically with the number of input points. The contributions of this work are twofold. First, we generalise kernels to be able to handle inputs in the form of areas, as well as points. This allows groups of spatially correlated data points to be condensed into a single entry, considerably reducing the size of the covariance matrix and enabling the method to deal efficiently with large amounts of data. Then, we create a mapping algorithm that makes use of Gaussian processes equipped with this kernel to build continuous occupancy maps. Experiments were conducted, using both synthetic and publicly available real data, to compare the presented algorithm with a similar previous method. They show it to be comparably accurate, yet considerably faster when dealing with large datasets

Dynamic Occupancy Grid Prediction for Urban Autonomous Driving: A Deep Learning Approach with Fully Automatic Labeling

Long-term situation prediction plays a crucial role in the development of intelligent vehicles. A major challenge still to overcome is the prediction of complex downtown scenarios with multiple road users, e.g., pedestrians, bikes, and motor vehicles, interacting with each other. This contribution tackles this challenge by combining a Bayesian filtering technique for environment representation, and machine learning as long-term predictor. More specifically, a dynamic occupancy grid map is utilized as input to a deep convolutional neural network. This yields the advantage of using spatially distributed velocity estimates from a single time step for prediction, rather than a raw data sequence, alleviating common problems dealing with input time series of multiple sensors. Furthermore, convolutional neural networks have the inherent characteristic of using context information, enabling the implicit modeling of road user interaction. Pixel-wise balancing is applied in the loss function counteracting the extreme imbalance between static and dynamic cells. One of the major advantages is the unsupervised learning character due to fully automatic label generation. The presented algorithm is trained and evaluated on multiple hours of recorded sensor data and compared to Monte-Carlo simulation

Value Iteration Networks on Multiple Levels of Abstraction

Learning-based methods are promising to plan robot motion without performing extensive search, which is needed by many non-learning approaches. Recently, Value Iteration Networks (VINs) received much interest since---in contrast to standard CNN-based architectures---they learn goal-directed behaviors which generalize well to unseen domains. However, VINs are restricted to small and low-dimensional domains, limiting their applicability to real-world planning problems. To address this issue, we propose to extend VINs to representations with multiple levels of abstraction. While the vicinity of the robot is represented in sufficient detail, the representation gets spatially coarser with increasing distance from the robot. The information loss caused by the decreasing resolution is compensated by increasing the number of features representing a cell. We show that our approach is capable of solving significantly larger 2D grid world planning tasks than the original VIN implementation. In contrast to a multiresolution coarse-to-fine VIN implementation which does not employ additional descriptive features, our approach is capable of solving challenging environments, which demonstrates that the proposed method learns to encode useful information in the additional features. As an application for solving real-world planning tasks, we successfully employ our method to plan omnidirectional driving for a search-and-rescue robot in cluttered terrain