Grid reference

Abstract: Pathfinding in uniform-cost grid environments is a problem commonly found in application areas such as robotics and video games. The state-of-the-art is dominated by hierarchical pathfinding algorithms which are fast and have small memory overheads but usually return suboptimal paths. In this paper we present a novel search strategy, specific to grids, which is fast, optimal and requires no memory overhead. Our algorithm can be described as a macro operator which identifies and selectively expands only certain nodes in a grid map which we call jump points. Intermediate nodes on a path connecting two jump points are never expanded. We prove that this approach always computes optimal solutions and then undertake a thorough empirical analysis, comparing our method with related works from the literature. We find that searching with jump points can speed up A* by an order of magnitude and more and report significant improvement over the current state of the art.

Abstract: In this research chapter, we present our work on a universal grid map library for use as mapping framework for mobile robotics. It is designed for a wide range of applications such as online surface reconstruction and terrain interpretation for rough terrain navigation. Our software features multi-layered maps, computationally efficient repositioning of the map boundaries, and compatibility with existing ROS map message types. Data storage is based on the linear algebra library Eigen, offering a wide range of data processing algorithms. This chapter outlines how to integrate the grid map library into the reader’s own applications. We explain the concepts and provide code samples to discuss various features of the software. As a use case, we present an application of the library for online elevation mapping with a legged robot. The grid map library and the robot-centric elevation mapping framework are available open-source at http://github.com/ethz-asl/grid_map and http://github.com/ethz-asl/elevation_mapping.

Abstract: Extracting semantic information solely from automotive radar data is a relatively new topic in the radar community. We present a complete pipeline to obtain semantic information for each target measured by a network of radar sensors. Static and dynamic objects are treated in two separate branches: In the first branch, a convolutional neural network performs semantic segmentation on radar grid maps of the static environment. In the second branch, a novel neural network architecture is used for recurrent instance segmentation on radar point clouds of moving objects. The class probabilities assigned to each cell in the grid map are mapped back to the radar targets in this spatial region so that in a merging step the results from the two classifiers can be combined into one point cloud. In addition to a novel network structure for recurrent instance segmentation of point clouds, we present a new set of layers for radar grid maps which are beneficial for semantic segmentation tasks and we also develop a weighting scheme for the network's loss function to account for the data integration process in grid maps. We evaluate our approaches on large data sets and we display that they outperform previously proposed methods.