How to Match Tracks of Visual Features for Automotive Long-Term SLAM

Abstract

Accurate localization is a vital prerequisite for future assistance or autonomous driving functions in intelligent vehicles. To achieve the required localization accuracy and availability, long-term visual SLAM algorithms like LLama-SLAM are a promising option. In such algorithms visual feature tracks, i. e. landmark observations over several consecutive image frames, have to be matched to feature tracks recorded days, weeks or months earlier. This leads to a more challenging matching problem than in short-term visual localization and known descriptor matching methods cannot be applied directly. In this paper, we devise several approaches to compare and match feature tracks and evaluate their performance on a long-term data set. With the proposed descriptor combination and masking ("CoMa") method the best track matching performance is achieved with minor computational cost. This method creates a single combined descriptor for each feature track and furthermore increases the robustness by capturing the appearance variations of this track in a descriptor mask.

The PRORETA 4 City Assistant System

“…The project was scheduled from 2015 to 2018 and four research assistants from three different institutes of TU Darmstadt worked together on this interdisciplinary project. Within this frame, several articles comprising new algorithms for driver intention detection and online driver adaptation [5][6][7][8][9], visual localization and mapping [10][11][12][13] and driver gaze target estimation [14][15][16][17] have been published as well as articles on safety approval of machine learning algorithms in the automotive context [18]. Many of the core ideas can be retrieved in the exemplary prototypical assistance system that is presented in this work.…”

“…Scenery Model: GNSS fused with speed and acceleration, digital HD-map, see Section 2.1 Camera-based long-term localization with LLama-SLAM, see [11,13]…”

“…This data is provided by a Kalman Filter that fuses GNSS 1 readings with speed and acceleration measurements from the vehicle sensors. To improve the system's capabilities, the ego-vehicle's position could also be determined by a camera-based long-term localization, which was also a research subject in PRORETA 4 [11,13]. All geographical coordinates inside the map as well as vehicle position and orientation are transformed from the common WGS84 coordinate system with latitude and longitude values in an UTM 2 -based coordinate system.…”

“…On the project's website, additional information about the project and the final presentation event can as well be found. The project's research results beyond the City Assistant System are published in [5][6][7][8][9][10][11][12][13][14][15][16][17][18].…”