Shape completion, the problem of estimating the complete geometry of objects from partial observations, lies at the core of many vision and robotics applications. In this work, we propose Point Completion Network (PCN), a novel learning-based approach for shape completion. Unlike existing shape completion methods, PCN directly operates on raw point clouds without any structural assumption (e.g. symmetry) or annotation (e.g. semantic class) about the underlying shape. It features a decoder design that enables the generation of fine-grained completions while maintaining a small number of parameters. Our experiments show that PCN produces dense, complete point clouds with realistic structures in the missing regions on inputs with various levels of incompleteness and noise, including cars from LiDAR scans in the KITTI dataset. Code, data and trained models are available at

Abstract-We present a novel approach for determining robot movements that efficiently accomplish the robot's tasks while not hindering the movements of people within the environment. Our approach models the goal-directed trajectories of pedestrians using maximum entropy inverse optimal control. The advantage of this modeling approach is the generality of its learned cost function to changes in the environment and to entirely different environments. We employ the predictions of this model of pedestrian trajectories in a novel incremental planner and quantitatively show the improvement in hindrancesensitive robot trajectory planning provided by our approach. I. INTRODUCTIONDetermining appropriate robotic actions in environments with moving people is a well-studied [15], [2], [5], but often difficult task due to the uncertainty of each person's future behavior. Robots should certainly never collide with people [11], but avoiding collisions alone is often unsatisfactory because the disruption of almost colliding can be burdensome to people and sub-optimal for robots. Instead, robots should predict the future locations of people and plan routes that will avoid such hindrances (i.e., situations where the person's natural behavior is disrupted due to a robot's proximity) while still efficiently achieving the robot's objectives. For example, given the origins and target destinations of the robot and person in Figure 1, the robot's hindrance-minimizing trajectory would take the longer way around the center obstacle (a table), leaving a clear path for the pedestrian.One common approach for predicting trajectories is to project the prediction step of a tracking filter [9], [13], [10] forward over time. For example, a Kalman filter's [7] future positions are predicted according to a Gaussian distribution with growing uncertainty and, unfortunately, often high probability for physically impossible locations (e.g., behind walls, within obstacles). Particle filters [16] can incorporate more sophisticated constraints and non-Gaussian distributions, but degrade into random walks of feasible motion over large time horizons rather than purposeful, goal-based motion. Closer to our research are approaches that directly model the policy [6]. These approaches assume that previously observed trajectories capture all purposeful behavior, and the only uncertainty involves determining to which previously observed class of trajectories the current behavior belongs. Models based on mixtures of trajectories and conditioned action distribution modeling (using hidden Markov models) have been employed [17]. This approach often suffers from over-fitting to the particular training trajectories and context of those trajectories. When changes to the environment occur (e.g., rearrangement of the furniture), the model will confidently predict incorrect trajectories through obstacles.

The detection and tracking of moving objects is an essential task in robotics. The CMU‐RI Navlab group has developed such a system that uses a laser scanner as its primary sensor. We will describe our algorithm and its use in several applications. Our system worked successfully on indoor and outdoor platforms and with several different kinds and configurations of two‐dimensional and three‐dimensional laser scanners. The applications vary from collision warning systems, people classification, observing human tracks, and input to a dynamic planner. Several of these systems were evaluated in live field tests and shown to be robust and reliable. © 2012 Wiley Periodicals, Inc.

The approach investigated in this work employs three-dimensional LADAR measurements to detect and track pedestrians over time. The sensor is employed on a moving vehicle. The algorithm quickly detects the objects which have the potential of being humans using a subset of these points, and then classifies each object using statistical pattern recognition techniques. The algorithm uses geometric and motion features to recognize human signatures. The perceptual capabilities described form the basis for safe and robust navigation in autonomous vehicles, necessary to safeguard pedestrians operating in the vicinity of a moving robotic vehicle.

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