Mobile robots operating on unstructured terrain must predict which areas of the environment they are able to pass in order to plan feasible paths and to react to unforeseen terrain patterns. We address traversability estimation as a heightmap classification problem: we build a convolutional neural network that, given an image representing the heightmap of a terrain patch, predicts whether the robot will be able to traverse such patch from left to right. The classifier is trained for a specific wheeled robot model (but may implement any other locomotion type such as tracked, legged, snake-like), using simulation data on a variety of procedurally generated training terrains; once trained, the classifier can quickly be applied to patches extracted from unseen large heightmaps, in multiple orientations, thus building oriented traversability maps. We quantitatively validate the approach on real-elevation datasets and implement a path planning approach that employs our traversability estimation.
Geometry-based approaches use local @cite_15 or global sensory data to derive an elevation map of the terrain, which is a convenient spatial representation for ground robots @cite_34 . Then, one option is to simulate a model of the robot on different areas of such elevation map: this allows one to explicitly test traversability, or, in a simpler setup, to evaluate the pose that the robot would assume when lying on each point of the elevation map @cite_31 . A more common approach evaluates each point of the elevation map by extracting simple local features (such as slope, roughness, step height), and then estimate traversability either through handcrafted rules, or using a learned classifier @cite_12 @cite_7 @cite_24 .
@cite_31 @cite_7 @cite_24 @cite_15 @cite_34 @cite_12
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We propose an information-theoretic planning approach that enables mobile robots to autonomously construct dense 3D maps in a computationally efficient manner. Inspired by prior work, we accomplish this task by formulating an information-theoretic objective function based on CauchySchwarz quadratic mutual information (CSQMI) that guides robots to obtain measurements in uncertain regions of the map. We then contribute a two stage approach for active mapping. First, we generate a candidate set of trajectories using a combination of global planning and generation of local motion primitives. From this set, we choose a trajectory that maximizes the information-theoretic objective. Second, we employ a gradientbased trajectory optimization technique to locally refine the chosen trajectory such that the CSQMI objective is maximized while satisfying the robot’s motion constraints. We evaluated our approach through a series of simulations and experiments on a ground robot and an aerial robot mapping unknown 3D environments. Real-world experiments suggest our approach reduces the time to explore an environment by 70 compared to a closest frontier exploration strategy and 57 compared to an information-based strategy that uses global planning, while simulations demonstrate the approach extends to aerial robots with higher-dimensional state. We address the problem of planning a path for a ground robot through unknown terrain, using observations from a flying robot. In search and rescue missions, which are our target scenarios, the time from arrival at the disaster site to the delivery of aid is critically important. Previous works required exhaustive exploration before path planning, which is time-consuming but eventually leads to an optimal path for the ground robot. Instead, we propose active exploration of the environment, where the flying robot chooses regions to map in a way that optimizes the overall response time of the system, which is the combined time for the air and ground robots to execute their missions. In our approach, we estimate terrain classes throughout our terrain map, and we also add elevation information in areas where the active exploration algorithm has chosen to perform 3-D reconstruction. This terrain information is used to estimate feasible and efficient paths for the ground robot. By exploring the environment actively, we achieve superior response times compared to both exhaustive and greedy exploration strategies. We demonstrate the performance and capabilities of the proposed system in simulated and real-world outdoor experiments. To the best of our knowledge, this is the first work to address ground robot path planning using active aerial exploration. We present a novel method for the localization of a legged robot on known terrain using only proprioceptive sensors such as joint encoders and an inertial measurement unit. In contrast to other proprioceptive pose estimation techniques, this method allows for global localization (i.e., localization with large initial uncertainty) without the use of exteroceptive sensors. This is made possible by establishing a measurement model based on the feasibility of putative poses on known terrain given observed joint angles and attitude measurements. Results are shown that demonstrate that the method performs better than dead-reckoning, and is also able to perform global localization from large initial uncertainty Accurately estimating a robot's pose relative to a global scene model and precisely tracking the pose in real-time is a fundamental problem for navigation and obstacle avoidance tasks. Due to the computational complexity of localization against a large map and the memory consumed by the model, state-of-the-art approaches are either limited to small workspaces or rely on a server-side system to query the global model while tracking the pose locally. The latter approaches face the problem of smoothly integrating the server's pose estimates into the trajectory computed locally to avoid temporal discontinuities. In this paper, we demonstrate that large-scale, real-time pose estimation and tracking can be performed on mobile platforms with limited resources without the use of an external server. This is achieved by employing map and descriptor compression schemes as well as efficient search algorithms from computer vision. We derive a formulation for integrating the global pose information into a local state estimator that produces much smoother trajectories than current approaches. Through detailed experiments, we evaluate each of our design choices individually and document its impact on the overall system performance, demonstrating that our approach outperforms state-of-the-art algorithms for localization at scale.