This letter presents three novel contributions for the 2D laser scan data. First, a low-level point feature detector for stable interest point detection. Second, an approach for high-level B-splines features extraction, and finally, a local descriptor for the interest points based on the shape context representation of the uniformly distributed points on the B-splines features. The clues of stable interest points obtained from the detector are used in the B-splines features extraction from the scan data, which enables lightweight and accurate mapping of the environment using fewer control points instead of scan data points. The letter also proposes benchmark metrics to evaluate the performance of the extracted high-level features in comparison with the state-of-the-art methodology. A robust descriptor for the interest points offers an efficient association and correspondence matching. Consistent results compared with the state-of-the-art FALKO-BSC and FLIRT- $\beta$ -Grid features in experiments with standard datasets prove the stability of our proposed interest point detector and the proficiency of the descriptor. The results of efficient localization using the pair are also presented.

Navigating a robot in a dynamic environment is a challenging task, especially when the behavior of other agents such as pedestrians, is only partially predictable. Also, the kinodynamic constraints on robot motion add an extra challenge. This paper proposes a novel navigational strategy for collision avoidance of a kinodynamically constrained robot from multiple moving passive agents with partially predictable behavior. Specifically, this paper presents a new approach to identify the set of control inputs to the robot, named control obstacle, which leads it towards a collision with a passive agent moving along an arbitrary path. The proposed method is developed by generalizing the concept of nonlinear velocity obstacle (NLVO), which is used to avoid collision with a passive agent, and takes into account the kinodynamic constraints on robot motion. Further, it formulates the navigational problem as an optimization problem, which allows the robot to make a safe decision in the presence of various sources of unmodelled uncertainties. Finally, the performance of the algorithm is evaluated for different parameters and is compared to existing velocity obstacle-based approaches. The simulated experiments show the excellent performance of the proposed approach in term of computation time and success rate.