A comparative evaluation of interest point detectors and local descriptors for visual SLAM

Abstract

In this paper we compare the behavior of different interest points detectors and descriptors under the conditions needed to be used as landmarks in visionbased simultaneous localization and mapping (SLAM). We evaluate the repeatability of the detectors, as well as the invariance and distinctiveness of the descriptors, under different perceptual conditions using sequences of images representing planar objects as well as 3D scenes. We believe that this information will be useful when selecting an appropriate landmark detector and descriptor for visual SLAM.

Illumination Tolerance for Visual Navigation with the Holistic Min-Warping Method

“…A number of feature detectors and descriptors have been developed, examples being the wide-spread FAST detector [6] and the SIFT [7] and SURF detectors and descriptors [8], and the modern binary descriptors such as BRIEF [9,10], ORB [11], or FREAK [12]. In robot navigation, matches between features are either used for estimating ego-motion between camera postures from two images (such as in visual odometry, see [13,14]), for estimating the metrical position of the corresponding landmark in a geometrical map [15][16][17][18], or for place recognition [19]. Together with subsequent outlier processing (like RANSAC) and n-point methods [14], feature-based methods can reliably estimate the relative posture between two images in 5 dimensions (up to scale).…”

“…However, we think that the insights on illumination tolerance for different distance measures gained in this paper are an important step to make holistic methods more competitive, at least for the min-warping method investigated here. Moreover, for feature-based methods, a number of studies have been dedicated to the comparative evaluation of different feature detectors and descriptors for different applications [1,2,5,[17][18][19] indicating that there may be no single method suitable for all applications. A systematic study exploring the application of feature-based methods for local visual homing with panoramic images is not yet available, and would also be beyond the scope of this paper.…”

“…In these methods, an image patch is first transformed into an invariant feature descriptor which is then matched by some simpler distance measure (reviews: [1][2][3][4][5][17][18][19]); here the burden of invariance (not only against illumination changes, but also against occlusions, spatial distortions, or noise) is imposed on the transformation. SIFT descriptors [7] comprise summed gradient magnitudes for different orientations in a histogram-like representation.…”

“…Binary feature vectors are compared by the Hamming distance. The comparative studies in [1,2,5,17] explicitly investigate illumination tolerance (albeit not for the modern binary descriptors).…”

Evaluation of Aligning Methods for Landmark-Based Maps in Visual SLAM

“…Furthermore these points are characterized by the U-SURF descriptor (Bay et al, 2006). The selection of the Harris Corner detector combined with the U-SURF descriptor is the result of a previous work, in which the aim was to find a suitable feature extractor for visual SLAM Martinez Mozos et al, 2007;Gil et al, 2009). The robots start at different positions and perform different trajectories in a 2D plane, sharing a common space in a typical office building.…”