Rapid Localization and Mapping Method Based on Adaptive Particle Filters

Abstract

With the development of autonomous vehicles, localization and mapping technologies have become crucial to equip the vehicle with the appropriate knowledge for its operation. In this paper, we extend our previous work by prepossessing a localization and mapping architecture for autonomous vehicles that do not rely on GPS, particularly in environments such as tunnels, under bridges, urban canyons, and dense tree canopies. The proposed approach is of two parts. Firstly, a K-means algorithm is employed to extract features from LiDAR scenes to create a local map of each scan. Then, we concatenate the local maps to create a global map of the environment and facilitate data association between frames. Secondly, the main localization task is performed by an adaptive particle filter that works in four steps: (a) generation of particles around an initial state (provided by the GPS); (b) updating the particle positions by providing the motion (translation and rotation) of the vehicle using an inertial measurement device; (c) selection of the best candidate particles by observing at each timestamp the match rate (also called particle weight) of the local map (with the real-time distances to the objects) and the distances of the particles to the corresponding chunks of the global map; (d) averaging the selected particles toderive the estimated position, and, finally, using a resampling method on the particles to ensure the reliability of the position estimation. The performance of the newly proposed technique is investigated on different sequences of the Kitti and Pandaset raw data with different environmental setups, weather conditions, and seasonal changes. The obtained results validate the performance of the proposed approach in terms of speed and representativeness of the feature extraction for real-time localization in comparison with other state-of-the-art methods.

Localization and Mapping for Self-Driving Vehicles: A Survey

“…Meanwhile, the shape of the vehicles was approximated with the help of the Euclidean clustering algorithm presented in [45]. Charroud et al [46,47] have removed the ground plan of all the LiDAR scan to reduce a huge amount of points, and they have used a Fuzzy K-means clustering technique to extract relevant features from the LiDAR scan. An extension of this work [48] adds a downsampling method to speed up the calculation process of the Fuzzy K-means algorithm.…”

“…-Semantic -Probabilistic -General -Probabilistic Calculation -Voxelisation -Building facades -Poles -Consume a lot -Middle -Hard -Low [42,43], [44,46], [47,48], [51,52], [53,54], [49,55], [56,57], [58,59], [60].…”

XDLL: Explained Deep Learning LiDAR-Based Localization and Mapping Method for Self-Driving Vehicles

“…An extension of this work was presented in the article [26], where the authors proposed a modified clustering particle filter that selects relevant particles to calculate the position by using sigma-point selection. Moreover, another extension of the work in [25] is the article [27], where it was proposed to extend the work on particle filters by selecting only the 10 best particles around the real position and regenerating the particles around them. This trick enables the particle filter to run fast and preserves the accuracy, as we generate particles close to the real position at each execution of the algorithm.…”