Point cloud registration refers to the problem of finding the rigid transformation that aligns two given point clouds, and is crucial for many applications in robotics and computer vision. The main insight of this paper is that we can directly optimize the point cloud registration problem without correspondences by utilizing an algorithmically simple, yet computationally complex, semi-exhaustive search approach that is very well-suited for parallelization on modern GPUs. Our proposed algorithm, Direct Semi-Exhaustive Search (DSES), iterates over potential rotation matrices and efficiently computes the inlier-maximizing translation associated with each rotation. It then computes the optimal rigid transformation based on any desired distance metric by directly computing the error associated with each transformation candidate {R,t}. By leveraging the parallelism of modern GPUs, DSES outperforms state-of-the-art methods for partial-to-full point cloud registration on the simulated ModelNet40 benchmark and demonstrates high performance and robustness for pose estimation on a real-world robotics problem (https://youtu.be/q0q2-s2KSuA).

We describe a mobile manipulation hardware and software system capable of autonomously performing complex human-level tasks in real homes, after being taught the task with a single demonstration from a person in virtual reality. This is enabled by a highly capable mobile manipulation robot, whole-body task space hybrid position/force control, teaching of parameterized primitives linked to a robust learned dense visual embeddings representation of the scene, and a task graph of the taught behaviors. We demonstrate the robustness of the approach by presenting results for performing a variety of tasks, under different environmental conditions, in multiple real homes. Our approach achieves 85% overall success rate on three tasks that consist of an average of 45 behaviors each. The video is available at: https://youtu.be/HSyAGMGikLk.

This paper describes the development of hard- ware, software, and algorithms for a prototype limbed robot capable of surface mobility on small bodies (asteroids and comets). It also describes the development of a laboratory testbed capable of simulating the micro-gravity and terrain of small bodies. A path following algorithm that uses visual odometry, robot body kinematics, and a variety of specialized gaits was used to demonstrate micro-gravity mobility with as few as 12 actuators. A mapping algorithm is also demonstrated that will enable path planning, limb trajectory planning, mobile grasping, and foot placement in future work. The results of this paper demonstrate that robust, stable, and precise small body mobility is feasible with a limbed robot.

We propose a method for learning using a set of feature representations which retrieve different amounts of information at different costs. The goal is to create a more efficient terrain classification algorithm which can be used in real-time, onboard an autonomous vehicle. Instead of building a monolithic classifier with uniformly complex representation for each class, the main idea here is to actively consider the labels or misclassification cost while constructing the classifier. For example, some terrain classes might be easily separable from the rest, so very simple representation will be sufficient to learn and detect these classes. This is taken advantage of during learning, so the algorithm automatically builds a variable-length visual representation which varies according to the complexity of the classification task. This enables fast recognition of different terrain types during testing. We also show how to select a set of feature representations so that the desired terrain classification task is accomplished with high accuracy and is at the same time efficient. The proposed approach achieves a good trade-off between recognition performance and speedup on data collected by an autonomous robot.

We address the problem of learning terrain traversability properties from visual input, using automatic mechanical supervision collected from sensors onboard an autonomous vehicle. We present a novel probabilistic framework in which the visual information and the mechanical supervision interact to learn particular terrain types and their properties. The proposed method is applied to learning of rover slippage from visual information in a completely automatic fashion. Our experiments show that using mechanical measurements as automatic supervision significantly improves the visual-based classification alone and approaches the results of learning with manual supervision. This work will enable the rover to drive safely on slopes, learning autonomously about different terrains and their slip characteristics.

In this paper we predict the amount of slip an exploration rover would experience using stereo imagery by learning from previous examples of traversing similar terrain. To do that, the information of terrain appearance and geometry regarding some location is correlated to the slip measured by the rover while this location is being traversed. This relationship is learned from previous experience, so slip can be predicted later at a distance from visual information only. The advantages of the approach are: 1) learning from examples allows the system to adapt to unknown terrains rather than using fixed heuristics or predefined rules; 2) the feedback about the observed slip is received from the vehicle's own sensors which can fully automate the process; 3) learning slip from previous experience can replace complex mechanical modeling of vehicle or terrain, which is time consuming and not necessarily feasible. Predicting slip is motivated by the need to assess the risk of getting trapped before entering a particular terrain. For example, a planning algorithm can utilize slip information by taking into consideration that a slippery terrain is costly or hazardous to traverse. A generic nonlinear regression framework is proposed in which the terrain type is determined from appearance and then a nonlinear model of slip is learned for a particular terrain type. In this paper we focus only on the latter problem and provide slip learning and prediction results for terrain types, such as soil, sand, gravel, and asphalt. The slip prediction error achieved is about 15% which is comparable to the measurement errors for slip itself

A system that enables continuous slip compensation for a Mars rover has been designed, implemented, and field-tested. This system is composed of several components that allow the rover to accurately and continuously follow a designated path, compensate for slippage, and reach intended goals in high-slip environments. These components include: visual odometry, vehicle kinematics, a Kalman filter pose estimator, and a slip compensation/path follower. Visual odometry tracks distinctive scene features in stereo imagery to estimate rover motion between successively acquired stereo image pairs. The vehicle kinematics for a rocker-bogie suspension system estimates motion by measuring wheel rates, and rocker, bogie, and steering angles. The Kalman filter merges data from an inertial measurement unit (IMU) and visual odometry. This merged estimate is then compared to the kinematic estimate to determine how much slippage has occurred, taking into account estimate uncertainties. If slippage has occurred then a slip vector is calculated by differencing the current Kalman filter estimate from the kinematic estimate. This slip vector is then used to determine the necessary wheel velocities and steering angles to compensate for slip and follow the desired path.

Tracked mobile robots in the 20 kg size class are under development for applications in urban reconnaissance. For efficient deployment, it is desirable for teams of robots to be able to automatically execute path following behaviors, with one or more followers tracking the path taken by a leader. The key challenges to enabling such a capability are (1) to develop sensor packages for such small robots that can accurately determine the path of the leader and (2) to develop path following algorithms for the subsequent robots. To date, we have integrated gyros, accelerometers, compass/inclinometers, odometry, and differential GPS into an effective sensing package. The paper describes the sensor package, sensor processing algorithm and path tracking algorithm we have developed for the leader/follower problem in small robots and shows the results of performance characterization of the system. We also document pragmatic lessons learned about design, construction, and electromagnetic interference issues particular to the performance of state sensors on small robots.

Small, tracked mobile robots designed for general urban mobility have been developed for the purpose of reconnaissance and/or search and rescue missions in buildings and cities. Autonomous stair climbing is a significant capability required for many of these missions. In this paper we present the design and implementation of a new set of estimation and control algorithms that increase the speed and effectiveness of stair climbing. We have developed: (1) a Kalman filter that fuses visual/laser data with inertial measurements and provides attitude estimates of improved accuracy at a high rate; and (2) a physics based controller that minimizes the heading error and maximizes the effective velocity of the vehicle during stair climbing. Experimental results using a tracked vehicle validate the improved performance of this control and estimation scheme over previous approaches.