Practical mapping and navigation solutions for large indoor environments continue to rely on relatively expensive range scanners, because of their accuracy, range and field of view. Microsoft Kinect on the other hand is inexpensive, is easy to use and has high resolution, but suffers from high noise, shorter range and a limiting field of view. We present a mapping and navigation system that uses the Microsoft Kinect sensor as the sole source of range data and achieves performance comparable to state-of-the-art LIDAR-based systems. We show how we circumvent the main limitations of Kinect to generate usable 2D maps of relatively large spaces and to enable robust navigation in changing and dynamic environments. We use the Benchmark for Robotic Indoor Navigation (BRIN) to quantify and validate the performance of our system.

We propose to overcome a significant limitation of the iterative closest point (ICP) algorithm used by KinectFusion, namely, its sole reliance upon geometric information. Our approach uses both geometric and color information in a direct manner that uses all the data in order to accurately estimate camera pose. Data association is performed by Lucas-Kanade to compute an affine warp between the color images associated with two RGBD point clouds. A subsequent step then estimates the Euclidean transformation between the point clouds using either a point-to-point or point-to-plane error metric, with a novel method based on a normal covariance test for automatically selecting between them. Together, Lucas-Kanade data association with covariance testing enables robust camera tracking through areas of low geometric features, without sacrificing accuracy in environments in which the existing ICP technique succeeds. Experimental results on several publicly available datasets demonstrate the improved performance both qualitatively and quantitatively.

We revisit the question of state space in the context of performing loop closure. Although a relative state space has been previously discounted, we show that such a state space is actually extremely powerful, able to achieve recognizable results after just one iteration. The power behind the technique (called POReSS) is the coupling between parameters that causes the orientation of one node to affect the position and orientation of other nodes. At the same time, the approach is fast because, like the more popular incremental state space, the Jacobian never needs to be explicitly computed. Furthermore, we show that while POReSS is able to quickly compute a solution near the global optimum, it is not precise enough to perform the fine adjustments necessary to reach the global minimum. As a result, we augment POReSS with a fast variant of Gauss-Seidel (called Graph-Seidel) on a global state space to allow the solution to settle closer to the global minimum. We show that this combination of POReSS and Graph-Seidel converges more quickly and scales to very large graphs better than other techniques while at the same time computing a competitive residual.

We propose to overcome a significant limitation of the KinectFusion algorithm, namely, its sole reliance upon geometric information to estimate camera pose. Our approach uses both geometric and color information in a direct manner that uses all the data in order to perform the association of data between two RGBD point clouds. Data association is performed by aligning the two color images associated with the two point clouds by estimating a projective warp using the Lucas-Kanade algorithm. This projective warp is then used to create a correspondence map between the two point clouds, which is then used as the data association for a point-to-plane error minimization. This approach to correspondence allows camera tracking to be maintained through areas of low geometric features. We show that our proposed LKDA data association technique enables accurate scene reconstruction in environments in which low geometric texture causes the existing approach to fail, while at the same time demonstrating that the new technique does not adversely affect results in environments in which the existing technique succeeds.

In this paper we present an algorithm for constructing nearly drift-free 3D occupancy grids of large indoor environments in an online manner. Our approach combines data from an odometry sensor with output from a visual registration algorithm, and it enforces a Manhattan world constraint by utilizing factor graphs to produce an accurate online estimate of the trajectory of a mobile robotic platform. We also examine the advantages and limitations of the octree data structure representation of a 3D environment. Through several experiments in environments with varying sizes and construction we show that our method reduces rotational and translational drift significantly without performing any loop closing techniques.