Planar structures are common in man-made environments. Their addition to monocular SLAM algorithms is of relevance in order to achieve more complete and higher- level scene representations. Also, the additional constraints they introduce might reduce the estimation errors in certain situations. In this paper we present a novel formulation to incorporate plane landmarks and planar constraints to feature- based monocular SLAM. Specifically, we enforce in-plane points to lie exactly in the plane they belong to, propagating such information to the rest of the states. Our formulation, differently from the state of the art, allows us to incorporate general planes, independently of depth information or CNN segmentation being available (although we could also use them). We evaluate our method in several sequences of public databases, showing accurate plane estimations and pose accuracy on par with state- of-the-art point-only monocular SLAM.

We consider the problem of next-best view selection for volumetric reconstruction of an object by a mobile robot equipped with a camera. Based on a probabilistic volumetric map that is built in real time, the robot can quantify the expected information gain from a set of discrete candidate views. We propose and evaluate several formulations to quantify this information gain for the volumetric reconstruction task, including visibility likelihood and the likelihood of seeing new parts of the object. These metrics are combined with the cost of robot movement in utility functions. The next best view is selected by optimizing these functions, aiming to maximize the likelihood of discovering new parts of the object. We evaluate the functions with simulated and real world experiments within a modular software system that is adaptable to other robotic platforms and reconstruction problems. We release our implementation open source.

This paper addresses the problem of simultaneous estimation of a vehicle's ego motion and motions of multiple moving objects in the scene-called eoru motions-through a monocular vehicle-mounted camera. Localization of multiple moving objects and estimation of their motions is crucial for autonomous vehicles. Conventional localization and mapping techniques (e.g., visual odometry and simultaneous localization and mapping) can only estimate the ego motion of the vehicle. The capability of a robot localization pipeline to deal with multiple motions has not been widely investigated in the literature. We present a theoretical framework for robust estimation of multiple relative motions in addition to the camera ego motion. First, the framework for general unconstrained motion is introduced and then it is adapted to exploit the vehicle kinematic constraints to increase efficiency. The method is based on projective factorization of the multiple-trajectory matrix. First, the ego motion is segmented and then several hypotheses are generated for the eoru motions. All the hypotheses are evaluated and the one with the smallest reprojection error is selected. The proposed framework does not need any a priori knowledge of the number of motions and is robust to noisy image measurements. The method with a constrained motion model is evaluated on a popular street-level image dataset collected in urban environments (the KITTI dataset), including several relative ego-motion and eoru-motion scenarios. A benchmark dataset (Hopkins 155) is used to evaluate this method with a general motion model. The results are compared with those of the state-of-the-art methods considering a similar problem, referred to as multibody structure from motion in the computer vision community.

In this paper, we tackle the problem of mapping multiple 3D rigid structures and estimating their motions from perspective views through a car-mounted camera. The proposed method complements conventional localization and mapping algorithms (such as Visual Odometry and SLAM) to estimate motions of other moving objects in addition to the vehicle's motion. We present a theoretical framework for robust estimation of multiple motions and structures from perspective images. The method is based on the factorization of the projective trajectory matrix without explicit estimation of projective depth values. We exploit the epipolar geometry of calibrated cameras to generate several hypotheses for motion segments. Once the hypotheses are obtained, they are evaluated in an iterative scheme by alternating between estimation of 3D structures and estimation of multiple motions. The proposed framework does not require any knowledge about the number of motions and is robust to noisy image measurements. The method is evaluated on street-level sequences from a car-mounted camera. A benchmark dataset is also used to compare the results with previous works, although most of the related works use synthetic scenes simulating desktop environments.

This paper presents a novel Photometric Stereo approach for static views that recasts the problem into a piecewise formulation. The proposed algorithm, called Piecewise Photometric Stereo (PPS), entails several advantages in respect to previous global approaches. It is intrinsically more efficient, since reconstructing the surface in patches is computationally faster than reconstructing the global surface. Each patch has been associated an individual photometric model rather than a single global model as used in classical approaches. In this way, the piecewise formulation may grasp more complex lighting effects. Finally, the global metric properties of the shape is preserved using the multi-view constraints. In this pipeline, structure from motion is exploited to define such set of constraints and to compose a 3D mesh representing the metric structure of the object. Real results with ground truth show the positive performance of our algorithm compared with a classical global approach for Photometric Stereo.