We present a novel mapping framework for robot navigation which features a multi-level querying system capable to obtain rapidly representations as diverse as a 3D voxel grid, a 2.5D height map and a 2D occupancy grid. These are inherently embedded into a memory and time efficient core data structure organized as a Tree of SkipLists. Compared to the well-known Octree representation, our approach exhibits a better time efficiency, thanks to its simple and highly parallelizable computational structure, and a similar memory footprint when mapping large workspaces. Peculiarly within the realm of mapping for robot navigation, our framework supports realtime erosion and re-integration of measurements upon reception of optimized poses from the sensor tracker, so as to improve continuously the accuracy of the map.
A quite popular memory efficient alternative to the 3D occupancy grid is the Octree @cite_10 , whereby the 3D Space is recursively partitioned into octants (octants being equivalent of quadrants in the 3D space) until voxels take the desired resolution. As such, this data structure is a in which each node has exactly 8 children; unlike the 3D grid, though, the Octree avoids modeling the empty space as only leaf and inner nodes associated with occupied space need to be allocated, thereby yielding significant memory savings when representing large environments. Hence, well-known mapping frameworks like Octomap @cite_11 rely on this kind of data structure to build the required workspace representation. In particular, Hornung @cite_11 shape their data structure so that voxels (leafs of the Octree) are the only nodes storing mapping information, all other ones containing references to children only. Therefore, the memory occupancy in bytes can be expressed as:
@cite_10 @cite_11
2726894975 2603429625 2784112303 1986503980
In this paper, we present an improved octree-based mapping framework for autonomous navigation of mobile robots. Octree is best known for its memory efficiency for representing large-scale environments. However, existing implementations, including the state-of-the-art OctoMap [1], are computationally too expensive for online applications that require frequent map updates and inquiries. Utilizing the sparse nature of the environment, we propose a ray tracing method with early termination for efficient probabilistic map update. We also propose a divide-and-conquer volume occupancy inquiry method which serves as the core operation for generation of free-space configurations for optimization-based trajectory generation. We experimentally demonstrate that our method maintains the same storage advantage of the original OctoMap, but being computationally more efficient for map update and occupancy inquiry. Finally, by integrating the proposed map structure in a complete navigation pipeline, we show autonomous quadrotor flight through complex environments. We present a deep convolutional decoder architecture that can generate volumetric 3D outputs in a compute- and memory-efficient manner by using an octree representation. The network learns to predict both the structure of the octree, and the occupancy values of individual cells. This makes it a particularly valuable technique for generating 3D shapes. In contrast to standard decoders acting on regular voxel grids, the architecture does not have cubic complexity. This allows representing much higher resolution outputs with a limited memory budget. We demonstrate this in several application domains, including 3D convolutional autoencoders, generation of objects and whole scenes from high-level representations, and shape from a single image. We present a dense volumetric simultaneous localisation and mapping (SLAM) framework that uses an octree representation for efficient fusion and rendering of either a truncated signed distance field (TSDF) or an occupancy map. The primary aim of this letter is to use one single representation of the environment that can be used not only for robot pose tracking and high-resolution mapping, but seamlessly for planning. We show that our highly efficient octree representation of space fits SLAM and planning purposes in a real-time control loop. In a comprehensive evaluation, we demonstrate dense SLAM accuracy and runtime performance on-par with flat hashing approaches when using TSDF-based maps, and considerable speed-ups when using occupancy mapping compared to standard occupancy maps frameworks. Our SLAM system can run at 10–40 Hz on a modern quadcore CPU, without the need for massive parallelization on a GPU. We, furthermore, demonstrate a probabilistic occupancy mapping as an alternative to TSDF mapping in dense SLAM and show its direct applicability to online motion planning, using the example of informed rapidly-exploring random trees (RRT @math ). Occupancy grids have been a popular mapping technique in mobile robotics for nearly 30 years. Occupancy grids offer a discrete representation of the world and seek to determine the occupancy probability of each cell. Traditional occupancy grid mapping methods make two assumptions for computational efficiency and it has been shown that the full posterior is computationally intractable for real-world mapping applications without these assumptions. The two assumptions result in tuning parameters that control the information gained from each distance measurement. In this paper, several tuning parameters found in the literature are optimized against the full posterior in 1D. In addition, this paper presents a new parameterization of the update function that outperforms existing methods in terms of capturing residual uncertainty. Capturing the residual uncertainty better estimates the position of obstacles and prevents under- and over-confidence in both the occupied and unoccupied cells. The paper concludes by showing that the new update function better captures the residual uncertainty in each cell when compared to an offline mapping method for realistic 2D simulations.