Tag Archives: Occupancy Grid Maps

A probabilistically rigurous formulation of the estimation of grid maps in dynamic scenarios, and a nice review and state-of-the-art of grid maps, both for static and dynamic scenarios

Dominik Nuss, Stephan Reuter, Markus Thom, Ting Yuan, Gunther Krehl, Michael Maile, Axel Gern, and Klaus Dietmayer, A random finite set approach for dynamic occupancy grid maps with real-time application, The International Journal of Robotics Research
Vol 37, Issue 8, pp. 841 – 866, DOI: 10.1177/0278364918775523.

Grid mapping is a well-established approach for environment perception in robotic and automotive applications. Early work suggests estimating the occupancy state of each grid cell in a robot’s environment using a Bayesian filter to recursively combine new measurements with the current posterior state estimate of each grid cell. This filter is often referred to as binary Bayes filter. A basic assumption of classical occupancy grid maps is a stationary environment. Recent publications describe bottom-up approaches using particles to represent the dynamic state of a grid cell and outline prediction-update recursions in a heuristic manner. This paper defines the state of multiple grid cells as a random finite set, which allows to model the environment as a stochastic, dynamic system with multiple obstacles, observed by a stochastic measurement system. It motivates an original filter called the probability hypothesis density / multi-instance Bernoulli (PHD/MIB) filter in a top-down manner. The paper presents a real-time application serving as a fusion layer for laser and radar sensor data and describes in detail a highly efficient parallel particle filter implementation. A quantitative evaluation shows that parameters of the stochastic process model affect the filter results as theoretically expected and that appropriate process and observation models provide consistent state estimation results.

A parallel implementation of a new probabilistic filter for occupancy grid maps that deals with non-static environments

Dominik Nuss, Stephan Reuter, Markus Thom, …, A random finite set approach for dynamic occupancy grid maps with real-time application, The International Journal of Robotics Research DOI: 10.1177/0278364918775523.

Grid mapping is a well-established approach for environment perception in robotic and automotive applications. Early work suggests estimating the occupancy state of each grid cell in a robot’s environment using a Bayesian filter to recursively combine new measurements with the current posterior state estimate of each grid cell. This filter is often referred to as binary Bayes filter. A basic assumption of classical occupancy grid maps is a stationary environment. Recent publications describe bottom-up approaches using particles to represent the dynamic state of a grid cell and outline prediction-update recursions in a heuristic manner. This paper defines the state of multiple grid cells as a random finite set, which allows to model the environment as a stochastic, dynamic system with multiple obstacles, observed by a stochastic measurement system. It motivates an original filter called the probability hypothesis density / multi-instance Bernoulli (PHD/MIB) filter in a top-down manner. The paper presents a real-time application serving as a fusion layer for laser and radar sensor data and describes in detail a highly efficient parallel particle filter implementation. A quantitative evaluation shows that parameters of the stochastic process model affect the filter results as theoretically expected and that appropriate process and observation models provide consistent state estimation results.

Rao-Blackwellized Particle Filter SLAM with grid maps in which particles do not contain the whole map but only a part

H. Jo, H. M. Cho, S. Jo and E. Kim, Efficient Grid-Based Rao–Blackwellized Particle Filter SLAM With Interparticle Map Sharing, IEEE/ASME Transactions on Mechatronics, vol. 23, no. 2, pp. 714-724, DOI: 10.1109/TMECH.2018.2795252.

In this paper, we propose a novel and efficient grid-based Rao-Blackwellized particle filter simultaneous localization and mapping (RBPF-SLAM) with interparticle map shaping (IPMS). The proposed method aims at saving the computational memory in the grid-based RBPF-SLAM while maintaining the mapping accuracy. Unlike conventional RBPF-SLAM in which each particle has its own map of the whole environment, each particle has only a small map of the nearby environment called an individual map in the proposed method. Instead, the map of the remaining large environment is shared by the particles. The part shared by the particles is called a base map. If the individual small maps become reliable enough to trust, they are merged with the base map. To determine when and which part of an individual map should be merged with the base map, we propose two map sharing criteria. Finally, the proposed IPMS RBPF-SLAM is applied to the real-world datasets and benchmark datasets. The experimental results show that our method outperforms conventional methods in terms of map accuracy versus memory consumption.