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Content Provider | IEEE Xplore Digital Library |
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Author | Nuss, D. Ting Yuan Krehl, G. Stuebler, M. Reuter, S. Dietmayer, K. |
Copyright Year | 2015 |
Description | Author affiliation: Inst. of Meas., Control, & Microtechnol., Ulm Univ., Ulm, Germany (Nuss, D.; Stuebler, M.; Reuter, S.; Dietmayer, K.) || Mercedes-Benz R&D North America, Inc., USA (Ting Yuan; Krehl, G.) |
Abstract | Occupancy grid mapping is a well-known environment perception approach. A grid map divides the environment into cells and estimates the occupancy probability of each cell based on sensor measurements. An important extension is the Bayesian occupancy filter (BOF), which additionally estimates the dynamic state of grid cells and allows modeling changing environments. In recent years, the BOF attracted more and more attention, especially sequential Monte Carlo implementations (SMC-BOF), requiring less computational costs. An advantage compared to classical object tracking approaches is the object-free representation of arbitrarily shaped obstacles and free-space areas. Unfortunately, publications about BOF based on laser measurements report that grid cells representing big, contiguous, stationary obstacles are often mistaken as moving with the velocity of the ego vehicle (ghost movements). This paper presents a method to fuse laser and radar measurement data with the SMC-BOF. It shows that the doppler information of radar measurements significantly improves the dynamic estimation of the grid map, reduces ghost movements, and in general leads to a faster convergence of the dynamic estimation. |
Starting Page | 1074 |
Ending Page | 1081 |
File Size | 1113906 |
Page Count | 8 |
File Format | |
e-ISBN | 9781467372664 |
DOI | 10.1109/IVS.2015.7225827 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2015-06-28 |
Publisher Place | South Korea |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Vehicle dynamics Estimation Probability density function Vehicles Atmospheric measurements Particle measurements Approximation methods |
Content Type | Text |
Resource Type | Article |
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