DESCRIPTOR SCORING FOR FEATURE SELECTION IN REAL-TIME VISUAL SLAM

Many emerging applications of Visual SLAM running on resource constrained hardware platforms impose very aggressive pose accuracy requirements and highly demanding latency constraints. To achieve the required pose accuracy under constrained compute budget, real-time SLAM implementations have to work with few but highly repeatable and invariant features. While many state-of-the-art techniques, proposed for selecting good features to track, do address some of these concerns, they are computationally complex and therefore, not suitable for power, latency and cost sensitive edge devices. On the other hand, simpler feature selection methods based on detector (corner) score, lack in identifying features with required invariance and trackability. We present a notion of feature descriptor score as a measure of invariance under distortions. We further propose feature selection method based on descriptor score requiring very minimal compute and demonstrate its performance with binary descriptors on an EKF based visual inertial odometry (VIO). Compared to detector score based methods, our method provides an improvement up to 10% in ATE (Absolute Trajectory Error) score on EuroC dataset.